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2024,47(6): 841-855, DOI: 10.13878/j.cnki.dqkxxb.20240311001
Abstract:
Extreme heat events and heat waves pose an increasingly significant threat to human communities,affecting public health,agriculture,economic stability and fueling secondary disasters such as wildfires.In recent years,heat waves have become more frequent,intense,and prolonged,particularly in the densely populated Northern Hemisphere (NH).However,the primary spatial modes of heat waves across the NH,along with their associated oceanic and atmospheric conditions remain insufficiently understood.This study investigates the natural variability of NH heat waves during boreal summer over the past century.We identify three leading modes in the frequency of daily maximum temperatures exceeding 35 ℃,collectively accounting for 52.6％ of the explained variance.The first mode presents a uniform pattern of heat wave frequency anomalies across most of the NH.This interdecadal mode corelates with the Atlantic Multi-decadal Oscillation (AMO),which triggers atmospheric anticyclone anomalies in the upper troposphere,decreasing cloud cover and increasing surface diabatic heating.AMO-induced Rossby wave energy propagates from low to mid-latitudes,then eastward across Eurasia and North America,establishing wave-train anomalies linked to this primary mode via teleconnections.The second mode,showing a latitudinal tripole pattern across Eurasia and a meridional dipole over North America,reflects interannual atmospheric variability tied to the North Atlantic Oscillation (NAO).The NAO influences critical North American regions through high-pressure ridges and propagating wave trains.The third mode captures an Eurasian meridional tripole and North American latitudinal dipole pattern,shaped by the Pacific Decadal Oscillation (PDO),El Niño-Southern Oscillation (ENSO),and sea surface temperature anomalies of the South Indian Ocean (SIO).Both PDO and ENSO affect NH heatwave frequency anomalies through upper-to-lower level geopotential height variations over Eurasia on interdecadal and interannual timescales,respectively.ENSO's influence extends to NH heat wave patterns via the Pacific-North America (PNA) teleconnection and the Indian Ocean capacitor effect.The SIO modulates vertical atmospheric motion over regions such as East Asia and eastern North America via Walker and Hadley circulations,further affecting NH heat wave frequency anomalies.We develop a multiple linear regression model to reconstruct NH heat wave frequencies based on the air-sea background factors of these three leading modes,including their spatial distributions and variance contributions.The model aligns well with observed heat wave frequencies and extreme high temperature events,reinforcing the significant impact of multi-scale oceanic and atmospheric signal on NH heat wave anomalies.When the absolute temperature thresholds are increased to 37 ℃ and 40 ℃,the leading modes display similar spatial patterns,suggesting that the identified oceanic and atmospheric drivers remain influential.Analysis based on relative temperature thresholds show consistent results,although variability at high latitudes exhibits a distinct contribution.Additional natural variability components,potentially linked to sea ice,snow cover,and soil moisture,warrant further investigation.
Extreme heat events and heat waves pose an increasingly significant threat to human communities,affecting public health,agriculture,economic stability and fueling secondary disasters such as wildfires.In recent years,heat waves have become more frequent,intense,and prolonged,particularly in the densely populated Northern Hemisphere (NH).However,the primary spatial modes of heat waves across the NH,along with their associated oceanic and atmospheric conditions remain insufficiently understood.This study investigates the natural variability of NH heat waves during boreal summer over the past century.We identify three leading modes in the frequency of daily maximum temperatures exceeding 35 ℃,collectively accounting for 52.6％ of the explained variance.The first mode presents a uniform pattern of heat wave frequency anomalies across most of the NH.This interdecadal mode corelates with the Atlantic Multi-decadal Oscillation (AMO),which triggers atmospheric anticyclone anomalies in the upper troposphere,decreasing cloud cover and increasing surface diabatic heating.AMO-induced Rossby wave energy propagates from low to mid-latitudes,then eastward across Eurasia and North America,establishing wave-train anomalies linked to this primary mode via teleconnections.The second mode,showing a latitudinal tripole pattern across Eurasia and a meridional dipole over North America,reflects interannual atmospheric variability tied to the North Atlantic Oscillation (NAO).The NAO influences critical North American regions through high-pressure ridges and propagating wave trains.The third mode captures an Eurasian meridional tripole and North American latitudinal dipole pattern,shaped by the Pacific Decadal Oscillation (PDO),El Niño-Southern Oscillation (ENSO),and sea surface temperature anomalies of the South Indian Ocean (SIO).Both PDO and ENSO affect NH heatwave frequency anomalies through upper-to-lower level geopotential height variations over Eurasia on interdecadal and interannual timescales,respectively.ENSO's influence extends to NH heat wave patterns via the Pacific-North America (PNA) teleconnection and the Indian Ocean capacitor effect.The SIO modulates vertical atmospheric motion over regions such as East Asia and eastern North America via Walker and Hadley circulations,further affecting NH heat wave frequency anomalies.We develop a multiple linear regression model to reconstruct NH heat wave frequencies based on the air-sea background factors of these three leading modes,including their spatial distributions and variance contributions.The model aligns well with observed heat wave frequencies and extreme high temperature events,reinforcing the significant impact of multi-scale oceanic and atmospheric signal on NH heat wave anomalies.When the absolute temperature thresholds are increased to 37 ℃ and 40 ℃,the leading modes display similar spatial patterns,suggesting that the identified oceanic and atmospheric drivers remain influential.Analysis based on relative temperature thresholds show consistent results,although variability at high latitudes exhibits a distinct contribution.Additional natural variability components,potentially linked to sea ice,snow cover,and soil moisture,warrant further investigation.
2024,47(6): 856-866, DOI: 10.13878/j.cnki.dqkxxb.20240430002
Abstract:
The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal variability in the tropics and has been widely studied since its discovery by Madden and Julian in 1971.The tropical oceans are a critical source of moisture,and positive sea surface temperature (SST) anomalies enhance heating and moisture fluxes,which facilitate the initialization and propagation of the MJO.Observations reveal a significant warming trend in the tropical Indian Ocean and western Pacific (TWIP) since 1979,associated with anthropogenic greenhouse gas forcing.However,previous studies indicate that current climate models struggle to simulate MJO variability accurately,underscoring the challenges in understanding its role in weather and the climate systems.This study examines the observed impacts of SST warming in the TWIP on the MJO,focusing on changes in its characteristics and their influence on winter precipitation in China from 1979 to 2012.South China,where precipitation is heaviest,has experienced a significant increase in rainfall since 1979,making it vital to investigate the contribution of MJO changes to this trend.The analysis reveals that SST warming in the TIWP intensifies heating and moisture fluxes,leading to an increase in MJO amplitude across all phases,with a statistical significant rise in phase 6.Vertical water vapor distribution changes result in an increased frequency of MJO phases,particularly phase 5,facilitating eastward propagation from phase 4 to phase 5 and prolonging phase 5 activity.These MJO changes are linked to enhanced subsidence in South China,associated with local Hadley cell dynamics,resulting in more negative sub seasonal precipitation anomalies during MJO phases 5 and 6 in recent decades.The findings provide observational evidence of the significant influence of SST warming on MJO activity and its subsequent impacts on regional precipitation patterns.The results highlight that MJO phase frequency changes reflect shifts in preferred regions of activity and structural adjustments.Future research should employ state-of-the-art GCMs to explore the influence of SST warming on MJO in greater detail.This study focuses on climatological changes in MJO under TWIP basin-wide warming.However,variations in MJO structure and their differential impacts on weather systems,warrant further diagnostic analysis.
The Madden-Julian Oscillation (MJO) is the dominant mode of intraseasonal variability in the tropics and has been widely studied since its discovery by Madden and Julian in 1971.The tropical oceans are a critical source of moisture,and positive sea surface temperature (SST) anomalies enhance heating and moisture fluxes,which facilitate the initialization and propagation of the MJO.Observations reveal a significant warming trend in the tropical Indian Ocean and western Pacific (TWIP) since 1979,associated with anthropogenic greenhouse gas forcing.However,previous studies indicate that current climate models struggle to simulate MJO variability accurately,underscoring the challenges in understanding its role in weather and the climate systems.This study examines the observed impacts of SST warming in the TWIP on the MJO,focusing on changes in its characteristics and their influence on winter precipitation in China from 1979 to 2012.South China,where precipitation is heaviest,has experienced a significant increase in rainfall since 1979,making it vital to investigate the contribution of MJO changes to this trend.The analysis reveals that SST warming in the TIWP intensifies heating and moisture fluxes,leading to an increase in MJO amplitude across all phases,with a statistical significant rise in phase 6.Vertical water vapor distribution changes result in an increased frequency of MJO phases,particularly phase 5,facilitating eastward propagation from phase 4 to phase 5 and prolonging phase 5 activity.These MJO changes are linked to enhanced subsidence in South China,associated with local Hadley cell dynamics,resulting in more negative sub seasonal precipitation anomalies during MJO phases 5 and 6 in recent decades.The findings provide observational evidence of the significant influence of SST warming on MJO activity and its subsequent impacts on regional precipitation patterns.The results highlight that MJO phase frequency changes reflect shifts in preferred regions of activity and structural adjustments.Future research should employ state-of-the-art GCMs to explore the influence of SST warming on MJO in greater detail.This study focuses on climatological changes in MJO under TWIP basin-wide warming.However,variations in MJO structure and their differential impacts on weather systems,warrant further diagnostic analysis.
2024,47(6): 867-880, DOI: 10.13878/j.cnki.dqkxxb.20231128002
Abstract:
In the context of global warming,future climate projections have become a major research focus.The increasing greenhouse gas (GHG) emissions in the CMIP6 scenarios-SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5 are accompanied by rising global mean surface air temperatures.However,the potential changes in the seasonal cycle of atmospheric mass,particularly the annual range,remain an open scientific question.This study analyzes CMIP6 data from 2015 to 2100 to examine the seasonal cycle characteristics of atmospheric mass under four GHG emission scenarios compared to historical runs.The study also examines the spatial distribution of surface air pressure and geopotential height in February and July,interpreted through temperature and wind fields.The results reveal that while the seasonal variations of global and hemispheric average atmospheric mass in the different scenarios resembles those in historical simulations,the annual range of hemispheric air fluctuates rather than increasing uniformly with higher GHG emissions.Specifically,the annual range decreases in SSP1-2.6 and SSP3-7.0,while it increases in SSP2-4.5 and SSP5-8.5.Globally,the annual range of atmospheric mass increases steadily with higher GHG emissions.Surface air pressure and geopotential height distributions in future scenarios align with reanalysis data,but in the Northern Hemisphere,a phase transition emerges,linked to the land-sea distribution dominated by Eurasia,the North Pacific,and the North Atlantic.In contrast,the Southern Hemisphere phase transitions occur along the latitudinal zones.The alternating positive and negative values in the February-July difference suggest regionally varying trends and amplitudes of change in surface air pressure and wind fields due to GHGs.These regional differences contribute to variations in the annual range of surface air pressure across the hemispheres.With increasing GHG emissions,the Northern and Southern Hemispheres respond differently across seasons,and this variation is evident not only in the lower troposphere but also at higher altitudes,including the stratosphere.The Arctic and Antarctic stratospheric vortices show nonlinear changes in intensity,weakening and strengthening in cycles as GHG emissions rise.These results suggest that the uneven distribution of GHGs in the atmosphere leads to variable temperature responses at different altitudes,altering the geopotential potential height field,layer thicknesses,and,consequently,surface air pressure.This highlights the region-specific response of atmospheric mass to increasing GHGs.The CMIP6 simulations provide valuable insights into atmospheric circulation changes under the conservation of atmospheric mass and their impacts on weather and climate in a warming world.Additionally,the nonlinear effects of human activities on water vapor mass and the atmospheric water-holding capacity deserve further exploration,particularly regarding their influence on the annual range of atmospheric mass.It remains uncertain whether transient and stable warming scenarios will lead to different outcomes in air mass changes.
In the context of global warming,future climate projections have become a major research focus.The increasing greenhouse gas (GHG) emissions in the CMIP6 scenarios-SSP1-2.6,SSP2-4.5,SSP3-7.0,and SSP5-8.5 are accompanied by rising global mean surface air temperatures.However,the potential changes in the seasonal cycle of atmospheric mass,particularly the annual range,remain an open scientific question.This study analyzes CMIP6 data from 2015 to 2100 to examine the seasonal cycle characteristics of atmospheric mass under four GHG emission scenarios compared to historical runs.The study also examines the spatial distribution of surface air pressure and geopotential height in February and July,interpreted through temperature and wind fields.The results reveal that while the seasonal variations of global and hemispheric average atmospheric mass in the different scenarios resembles those in historical simulations,the annual range of hemispheric air fluctuates rather than increasing uniformly with higher GHG emissions.Specifically,the annual range decreases in SSP1-2.6 and SSP3-7.0,while it increases in SSP2-4.5 and SSP5-8.5.Globally,the annual range of atmospheric mass increases steadily with higher GHG emissions.Surface air pressure and geopotential height distributions in future scenarios align with reanalysis data,but in the Northern Hemisphere,a phase transition emerges,linked to the land-sea distribution dominated by Eurasia,the North Pacific,and the North Atlantic.In contrast,the Southern Hemisphere phase transitions occur along the latitudinal zones.The alternating positive and negative values in the February-July difference suggest regionally varying trends and amplitudes of change in surface air pressure and wind fields due to GHGs.These regional differences contribute to variations in the annual range of surface air pressure across the hemispheres.With increasing GHG emissions,the Northern and Southern Hemispheres respond differently across seasons,and this variation is evident not only in the lower troposphere but also at higher altitudes,including the stratosphere.The Arctic and Antarctic stratospheric vortices show nonlinear changes in intensity,weakening and strengthening in cycles as GHG emissions rise.These results suggest that the uneven distribution of GHGs in the atmosphere leads to variable temperature responses at different altitudes,altering the geopotential potential height field,layer thicknesses,and,consequently,surface air pressure.This highlights the region-specific response of atmospheric mass to increasing GHGs.The CMIP6 simulations provide valuable insights into atmospheric circulation changes under the conservation of atmospheric mass and their impacts on weather and climate in a warming world.Additionally,the nonlinear effects of human activities on water vapor mass and the atmospheric water-holding capacity deserve further exploration,particularly regarding their influence on the annual range of atmospheric mass.It remains uncertain whether transient and stable warming scenarios will lead to different outcomes in air mass changes.
2024,47(6): 881-891, DOI: 10.13878/j.cnki.dqkxxb.20240830002
Abstract:
The Yangtze River basin(YRB) is a key region for the occurrence of extreme high temperatures (EHT),which has significant impacts on both human society and ecosystems.The number of EHT days (EHTD) in the YRB exhibits notable interannual variability.Using sea surface temperature (SST) data from the Hadley Center,daily maximum temperature datasets from CN05.1,and reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR),this study investigates the relationship between summer North Atlantic SST anomalies (SSTA) and EHTD in the YRB through singular value decomposition (SVD) analysis.Results reveal that the first SVD mode demonstrates a strong relationship between North Atlantic Dipole (NAD) pattern (characterized by positive (negative) SSTAs in the midlatitude and negative (positive) SSTAS in the subtropical North Atlantic) and EHTD in the YRB,accounting for 63.43％ of the total squared covariance.The correlation coefficient between the time series of the first SVD mode for NAD SSTA and EHTD is 0.51,statistically significant at the 99％ confidence level.An interdecadal shift in the relationship between NAD SSTA and EHTD in the YRB occurred in the late 1980s.From 1961—1988,NAD SSTA showed a strong connection with EHTD in the YRB.However,after the late 1980s,this relationship weakened.Further analysis suggests that the interdecadal changes in the NAD SSTA-EHTD relationship are primarily driven by differences in atmospheric circulation anomalies over Eurasia,triggered by NAD SSTA.Before the late 1980s,positive (negative) NAD SSTA induced an atmospheric teleconnection pattern extending from the North Atlantic to East Asia in the middle and upper troposphere,featuring two positive (negative) geopotential height anomaly centers over the Ural Mountains and East Asia,and two negative (positive) centers over Greenland and Lake Baikal.This teleconnection pattern,linked to Rossby wave energy propagation,led to an anticyclone/cyclone circulation anomaly over East Asia,inducing anomalous descending/ascending motion over the YRB,which in turn provided favorable/unfavorable thermal conditions for EHT occurrences.The East Asian anticyclone/cyclone circulation anomaly had a significant influence on EHTD in the YRB.However,after the late 1980s,the aforementioned teleconnection pattern excited by NAD SSTA weakened,reducing the East Asian anticyclone/cyclone circulation anomaly and weakening the NAD SSTA-EHTD relationship.The differences in atmospheric circulation anomalies between the two periods are closely related to the reduced amplitude of NAD SSTA.The reasons behind these weakening of NAD SSTA amplitude remain to be explored.These findings offer valuable insights for understanding and predicting EHTD in the YRB.
The Yangtze River basin(YRB) is a key region for the occurrence of extreme high temperatures (EHT),which has significant impacts on both human society and ecosystems.The number of EHT days (EHTD) in the YRB exhibits notable interannual variability.Using sea surface temperature (SST) data from the Hadley Center,daily maximum temperature datasets from CN05.1,and reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR),this study investigates the relationship between summer North Atlantic SST anomalies (SSTA) and EHTD in the YRB through singular value decomposition (SVD) analysis.Results reveal that the first SVD mode demonstrates a strong relationship between North Atlantic Dipole (NAD) pattern (characterized by positive (negative) SSTAs in the midlatitude and negative (positive) SSTAS in the subtropical North Atlantic) and EHTD in the YRB,accounting for 63.43％ of the total squared covariance.The correlation coefficient between the time series of the first SVD mode for NAD SSTA and EHTD is 0.51,statistically significant at the 99％ confidence level.An interdecadal shift in the relationship between NAD SSTA and EHTD in the YRB occurred in the late 1980s.From 1961—1988,NAD SSTA showed a strong connection with EHTD in the YRB.However,after the late 1980s,this relationship weakened.Further analysis suggests that the interdecadal changes in the NAD SSTA-EHTD relationship are primarily driven by differences in atmospheric circulation anomalies over Eurasia,triggered by NAD SSTA.Before the late 1980s,positive (negative) NAD SSTA induced an atmospheric teleconnection pattern extending from the North Atlantic to East Asia in the middle and upper troposphere,featuring two positive (negative) geopotential height anomaly centers over the Ural Mountains and East Asia,and two negative (positive) centers over Greenland and Lake Baikal.This teleconnection pattern,linked to Rossby wave energy propagation,led to an anticyclone/cyclone circulation anomaly over East Asia,inducing anomalous descending/ascending motion over the YRB,which in turn provided favorable/unfavorable thermal conditions for EHT occurrences.The East Asian anticyclone/cyclone circulation anomaly had a significant influence on EHTD in the YRB.However,after the late 1980s,the aforementioned teleconnection pattern excited by NAD SSTA weakened,reducing the East Asian anticyclone/cyclone circulation anomaly and weakening the NAD SSTA-EHTD relationship.The differences in atmospheric circulation anomalies between the two periods are closely related to the reduced amplitude of NAD SSTA.The reasons behind these weakening of NAD SSTA amplitude remain to be explored.These findings offer valuable insights for understanding and predicting EHTD in the YRB.
2024,47(6): 892-903, DOI: 10.13878/j.cnki.dqkxxb.20230824001
Abstract:
Hunan Province,located in southern China,experiences frequent and intense precipitation,especially during the rainy season.Recent years,have seen an increase in extreme precipitation events,characterized by significant diurnal variation,which complicates forecasting efforts.Over the past decade,China has developed a regional network of high-resolution,fully-automated weather stations,enhancing the study the study short-duration heavy rainfall.This study utilizes hourly precipitation data from 1 599 automatic weather stations across Hunan Province,collected from 2012 to 2021,to analyze the characteristics of hourly extreme precipitation during the rainy season (April—September).The study also examines the relationship between hourly extreme precipitation events and 12-hour rainstorms,focusing on their statistical characteristics and the contribution of hourly extreme events to overall rainstorm totals.This quantitative analysis aims to reveal the intrinsic connections between these events and provide a technical foundation for improving nowcasting and early warning systems.The 99.9％ percentile was selected as the threshold for defining extreme hourly precipitation in Hunan.Results show that the spatial distributions of frequency and intensity of extreme precipitation events are similar,with high-frequency areas concentrated in the Xuefeng Mountains,the southern Nanling Mountains,and the Dongting Lake area,with maximum values in southern Hunan.Complex terrain and underlying surfaces significantly enhance hourly precipitation intensity.The annual frequency of extreme hourly events in the rainy season exhibits wavelike growth,peaking in 2021 and reaching a low in 2013,where 2021 recorded nearly 90％ more events than in 2013.Extreme precipitation events are most common from May to August,peaking in June.Diurnally,extreme precipitation follows a bimodal pattern,peaking at 18:00 and 07:00 BST,with a rapid increase in the afternoon,a peak in the evening,a gradual decline overnight,and a secondary peak in the early morning.The spatial distribution of 12-hour rainstorm is similar to that of hourly extreme events,with an average annual frequency of 2 490 occurrences at night and 2 039 during the day,indicating a higher nighttime frequency.High-frequency areas for 12-hour rainstorms are found primarily in western Hunan,along the Xuefeng and Wuling Mountain ranges,with isolated high-frequency locations in the eastern and southern regions,while lower frequencies are observed between 25.5°N and 26.5°N.Monthly and daily variations in 12-hour rainstorm frequency show distinct regional patterns,with high-frequency zones varying from May to September and the Dongting Lake region experiencing the highest frequency in June and July.Daytime hourly extreme precipitation contributes significantly more to 12-hour rainstorms than nighttime events:in the daytime,41％ of stations report that hourly extreme events contribute 70％—90％ of 12-hour rainstorm totals,while this contribution rate is only 25％ at night.High-contribution regions are primarily concentrated in southern Hunan.
Hunan Province,located in southern China,experiences frequent and intense precipitation,especially during the rainy season.Recent years,have seen an increase in extreme precipitation events,characterized by significant diurnal variation,which complicates forecasting efforts.Over the past decade,China has developed a regional network of high-resolution,fully-automated weather stations,enhancing the study the study short-duration heavy rainfall.This study utilizes hourly precipitation data from 1 599 automatic weather stations across Hunan Province,collected from 2012 to 2021,to analyze the characteristics of hourly extreme precipitation during the rainy season (April—September).The study also examines the relationship between hourly extreme precipitation events and 12-hour rainstorms,focusing on their statistical characteristics and the contribution of hourly extreme events to overall rainstorm totals.This quantitative analysis aims to reveal the intrinsic connections between these events and provide a technical foundation for improving nowcasting and early warning systems.The 99.9％ percentile was selected as the threshold for defining extreme hourly precipitation in Hunan.Results show that the spatial distributions of frequency and intensity of extreme precipitation events are similar,with high-frequency areas concentrated in the Xuefeng Mountains,the southern Nanling Mountains,and the Dongting Lake area,with maximum values in southern Hunan.Complex terrain and underlying surfaces significantly enhance hourly precipitation intensity.The annual frequency of extreme hourly events in the rainy season exhibits wavelike growth,peaking in 2021 and reaching a low in 2013,where 2021 recorded nearly 90％ more events than in 2013.Extreme precipitation events are most common from May to August,peaking in June.Diurnally,extreme precipitation follows a bimodal pattern,peaking at 18:00 and 07:00 BST,with a rapid increase in the afternoon,a peak in the evening,a gradual decline overnight,and a secondary peak in the early morning.The spatial distribution of 12-hour rainstorm is similar to that of hourly extreme events,with an average annual frequency of 2 490 occurrences at night and 2 039 during the day,indicating a higher nighttime frequency.High-frequency areas for 12-hour rainstorms are found primarily in western Hunan,along the Xuefeng and Wuling Mountain ranges,with isolated high-frequency locations in the eastern and southern regions,while lower frequencies are observed between 25.5°N and 26.5°N.Monthly and daily variations in 12-hour rainstorm frequency show distinct regional patterns,with high-frequency zones varying from May to September and the Dongting Lake region experiencing the highest frequency in June and July.Daytime hourly extreme precipitation contributes significantly more to 12-hour rainstorms than nighttime events:in the daytime,41％ of stations report that hourly extreme events contribute 70％—90％ of 12-hour rainstorm totals,while this contribution rate is only 25％ at night.High-contribution regions are primarily concentrated in southern Hunan.
2024,47(6): 904-916, DOI: 10.13878/j.cnki.dqkxxb.20230811001
Abstract:
Vapor pressure deficit (VPD) is a critical meteorological variable with significant implications for hydrology,ecology,and climate change.Numerous studies have demonstrated that VPD,a key indicator of atmospheric dryness,has undergone substantial changes in response to global climate warming.Investigating the spatiotemporal variations of VPD across different regions of China is essential for advancing climate change research and supporting agricultural and pastoral practices.While regional-scale VPD studies have been conducted by domestic scholars,comparative analysis of VPD changes at the national scale remain limited.This study uses daily mean air temperature and relative humidity data from 772 meteorological stations across China,applying Tetens' empirical formula to calculate VPD.Through co-Kriging interpolation,Mann-Kendall trend tests,and correlation and partial correlation analyses,the spatiotemporal variability and trends of VPD across different time scales and geographical regions from 1961 to 2015 were examined.Additionally,the study explores the relationships between VPD,temperature,and relative humidity,identifying the dominant factors influencing VPD changes over different periods.The results indicate significant spatiotemporal variation in VPD across China from 1961 to 2015,with higher values in summer and lower values in winter.Spatially,high annual average VPD values were concentrated in the arid and semi-arid regions of Northwest China,while lower values were found in Northeast China,the Qinghai-Xizang Plateau,and parts of the southern region.The annual average VPD across China ranged from 0.127 kPa to 1.547 kPa,with an overall average of 0.526 kPa.Most regions showed an increasing VPD trend over time.Between 1961 and 1999,57％ stations exhibited increasing VPD trends,and this proportion rose to 67％ between 2000 and 2015,with the year 2000 identified as a mutation point.The average VPD increase accelerated from 1.519 6 hPa/(10 a) to 7.074 3 hPa/(10 a) after 2000.Correlation and partial correlation analyses revealed that VPD was significantly positively correlated with temperature and negatively correlated with relative humidity in all regions.Maximum temperature was the dominant factor driving VPD changes in southern and northern China,while average temperature and relative humidity played a greater role in the Northwest and Qinghai-Xizang regions.These dominant factors remained consistent before and after the mutation point.While the primary factors influencing VPD changes were explored,further investigation is needed to assess the impact of other potential factors such as topography,vegetation cover,and broader climatic conditions.This study provides insights into the characteristics and drivers of VPD changes across seasons and regions,offering a scientific foundation for climate-ecological and vegetation modeling studies and valuable guidance for policy-makers and relevant stakeholders.
Vapor pressure deficit (VPD) is a critical meteorological variable with significant implications for hydrology,ecology,and climate change.Numerous studies have demonstrated that VPD,a key indicator of atmospheric dryness,has undergone substantial changes in response to global climate warming.Investigating the spatiotemporal variations of VPD across different regions of China is essential for advancing climate change research and supporting agricultural and pastoral practices.While regional-scale VPD studies have been conducted by domestic scholars,comparative analysis of VPD changes at the national scale remain limited.This study uses daily mean air temperature and relative humidity data from 772 meteorological stations across China,applying Tetens' empirical formula to calculate VPD.Through co-Kriging interpolation,Mann-Kendall trend tests,and correlation and partial correlation analyses,the spatiotemporal variability and trends of VPD across different time scales and geographical regions from 1961 to 2015 were examined.Additionally,the study explores the relationships between VPD,temperature,and relative humidity,identifying the dominant factors influencing VPD changes over different periods.The results indicate significant spatiotemporal variation in VPD across China from 1961 to 2015,with higher values in summer and lower values in winter.Spatially,high annual average VPD values were concentrated in the arid and semi-arid regions of Northwest China,while lower values were found in Northeast China,the Qinghai-Xizang Plateau,and parts of the southern region.The annual average VPD across China ranged from 0.127 kPa to 1.547 kPa,with an overall average of 0.526 kPa.Most regions showed an increasing VPD trend over time.Between 1961 and 1999,57％ stations exhibited increasing VPD trends,and this proportion rose to 67％ between 2000 and 2015,with the year 2000 identified as a mutation point.The average VPD increase accelerated from 1.519 6 hPa/(10 a) to 7.074 3 hPa/(10 a) after 2000.Correlation and partial correlation analyses revealed that VPD was significantly positively correlated with temperature and negatively correlated with relative humidity in all regions.Maximum temperature was the dominant factor driving VPD changes in southern and northern China,while average temperature and relative humidity played a greater role in the Northwest and Qinghai-Xizang regions.These dominant factors remained consistent before and after the mutation point.While the primary factors influencing VPD changes were explored,further investigation is needed to assess the impact of other potential factors such as topography,vegetation cover,and broader climatic conditions.This study provides insights into the characteristics and drivers of VPD changes across seasons and regions,offering a scientific foundation for climate-ecological and vegetation modeling studies and valuable guidance for policy-makers and relevant stakeholders.
2024,47(6): 917-927, DOI: 10.13878/j.cnki.dqkxxb.20240104001
Abstract:
The near-surface air temperature lapse rate (NTLR) is a crucial yet complex meteorological parameter that determines the rate at which air temperature decreases with increasing altitude.A uniform lapse rate across the troposphere cannot adequately capture the spatiotemporal variability of NTLR.Given the significant influences of topography,geographical location,seasonal cycles,diurnal variations,and human activities on NTLR,this study highlights the need for a spatiotemporally specific approach to accurately characterize these variations.The study has two primary objectives:first,to analyze the spatial and temporal variability of NTLR across different regions of China,and second,to assess the impact of these variations on the correction of temperature forecast biases in the NCEP model.According to comprehensive physical regionalization,China is divided into 33 sub-regions.Utilizing a dataset of hourly temperature observations from 2 427 national-level meteorological stations in China,covering the period from March 1,2013,to February 28,2022,the study calculates regional and seasonal NTLR values for both day and night using a ternary linear regression model.This model accounts for altitude,latitude,and longitude,providing a detailed representation of the NTLR’s relationship with geographical factors.The calculated NTLR values are then applied to correct elevation-based biases in the NCEP temperature forecasts from March 1,2022,to February 28,2023.The results indicate that:1) Significant spatiotemporal variations in NTLR exist across China,with differences occurring even within the same sub-region.The annual mean NTLR for China is 0.57 ℃/(100 m),with nighttime values slightly higher than daytime values.2) Seasonal and diurnal variations in NTLR are substantial,with the highest rates observed in summer (average 0.63 ℃/(100 m)) and the lowest in winter (average 0.47 ℃/(100 m)).The maximum day-night differences in NTLR are 0.22,0.29,0.28,and 0.50 ℃/(100 m) for spring,summer,autumn,and winter,respectively.3) Applying the statistically derived NTLR to correct elevation-based biases in the NCEP temperature forecasts reduces the mean absolute error (MAE) across China for all forecast periods up to 360 h,with an average reduction of 0.56 ℃ and a maximum reduction of 1.20 ℃.The correction is most effective in summer and during the daytime across most sub-regions.However,in some areas,such as the central and northern Qinghai-Xizang Plateau,applying NTLR for bias correction has led to reduced forecast accuracy,indicating that NTLR application must be adapted to local conditions.Future research should explore the impact of varying altitude ranges,slope aspects,and other factors on NTLR within the context of comprehensive physical regionalization.
The near-surface air temperature lapse rate (NTLR) is a crucial yet complex meteorological parameter that determines the rate at which air temperature decreases with increasing altitude.A uniform lapse rate across the troposphere cannot adequately capture the spatiotemporal variability of NTLR.Given the significant influences of topography,geographical location,seasonal cycles,diurnal variations,and human activities on NTLR,this study highlights the need for a spatiotemporally specific approach to accurately characterize these variations.The study has two primary objectives:first,to analyze the spatial and temporal variability of NTLR across different regions of China,and second,to assess the impact of these variations on the correction of temperature forecast biases in the NCEP model.According to comprehensive physical regionalization,China is divided into 33 sub-regions.Utilizing a dataset of hourly temperature observations from 2 427 national-level meteorological stations in China,covering the period from March 1,2013,to February 28,2022,the study calculates regional and seasonal NTLR values for both day and night using a ternary linear regression model.This model accounts for altitude,latitude,and longitude,providing a detailed representation of the NTLR’s relationship with geographical factors.The calculated NTLR values are then applied to correct elevation-based biases in the NCEP temperature forecasts from March 1,2022,to February 28,2023.The results indicate that:1) Significant spatiotemporal variations in NTLR exist across China,with differences occurring even within the same sub-region.The annual mean NTLR for China is 0.57 ℃/(100 m),with nighttime values slightly higher than daytime values.2) Seasonal and diurnal variations in NTLR are substantial,with the highest rates observed in summer (average 0.63 ℃/(100 m)) and the lowest in winter (average 0.47 ℃/(100 m)).The maximum day-night differences in NTLR are 0.22,0.29,0.28,and 0.50 ℃/(100 m) for spring,summer,autumn,and winter,respectively.3) Applying the statistically derived NTLR to correct elevation-based biases in the NCEP temperature forecasts reduces the mean absolute error (MAE) across China for all forecast periods up to 360 h,with an average reduction of 0.56 ℃ and a maximum reduction of 1.20 ℃.The correction is most effective in summer and during the daytime across most sub-regions.However,in some areas,such as the central and northern Qinghai-Xizang Plateau,applying NTLR for bias correction has led to reduced forecast accuracy,indicating that NTLR application must be adapted to local conditions.Future research should explore the impact of varying altitude ranges,slope aspects,and other factors on NTLR within the context of comprehensive physical regionalization.
2024,47(6): 928-948, DOI: 10.13878/j.cnki.dqkxxb.20231129002
Abstract:
Rapid urbanization in China has exacerbated atmospheric pollution, particularly in urban areas.Urban lakes played a crucial role in moderating local climate and air quality by influencing atmospheric boundary layer circulation and pollutant transport mechanisms.However, previous studies have relied primarily on mesoscale models, where the influence of additional coupled mechanisms may impact the accuracy of sensitivity results.To address this, we utilized a conceptual urban land surface model to conduct suburban-scale sensitivity experiments, isolating the primary factors and mechanisms by which urban lakes influence the distribution and transport of near-surface pollutants.Focusing on the diurnal patterns of pollutant mass concentrations at lakeside and non-lakeside urban stations in Nanjing during the summer, we found notable differences in pollutant behavior.For NO2, a ground-emitted pollutant, lakeside stations recorded a daytime average concentration of (1.64 ±0.29) μg·m-3 higher than non-lakeside, while nighttime concentrations were (0.51 ±1.39) μg·m-3 lower.In contrast, O3, which forms at mid-and upper-boundary layers, exhibited lower daytime concentrations by (9.57 ±2.19) μg·m-3 at lakeside stations, with nighttime levels (1.24 ±4.68) μg·m-3 higher.No significant differences were found for PM2.5 concentrations.Using a two-dimensional land surface model, we conducted sensitivity experiments to examine the effects of lake presence and lake-to-urban land distribution under different emission scenarios.Simulations indicated that the model accurately reproduced key temperature and pollutant mass concentrations patterns, comparable to more complex mesoscale model results.Thermal property differences between lake and urban land surfaces significantly impacted low-level atmospheric circulation and vertical stability, altering pollutant diffusion and transport.Daytime thermal stability and limited vertical diffusion over lakeside areas led to a higher concentrations of surface-emitted pollutants near lakes, while concentrations of pollutants formed at mid-levels were lower compared to non-lakeside areas;this pattern reversed at night.Simulation outcomes aligned well with observed data trends and were qualitatively consistent with WRF-Chem results.While emphasizing rigorous monitoring and data processing methods, spatial heterogeneity of urban structures and emissions, as well as observational data limitations, introduced some uncertainties.Future research should incorporate more extensive data from diverse urban regions to better generalize these patterns and strengthen the theoretical foundation for air quality management in urban lakeside environments.
Rapid urbanization in China has exacerbated atmospheric pollution, particularly in urban areas.Urban lakes played a crucial role in moderating local climate and air quality by influencing atmospheric boundary layer circulation and pollutant transport mechanisms.However, previous studies have relied primarily on mesoscale models, where the influence of additional coupled mechanisms may impact the accuracy of sensitivity results.To address this, we utilized a conceptual urban land surface model to conduct suburban-scale sensitivity experiments, isolating the primary factors and mechanisms by which urban lakes influence the distribution and transport of near-surface pollutants.Focusing on the diurnal patterns of pollutant mass concentrations at lakeside and non-lakeside urban stations in Nanjing during the summer, we found notable differences in pollutant behavior.For NO2, a ground-emitted pollutant, lakeside stations recorded a daytime average concentration of (1.64 ±0.29) μg·m-3 higher than non-lakeside, while nighttime concentrations were (0.51 ±1.39) μg·m-3 lower.In contrast, O3, which forms at mid-and upper-boundary layers, exhibited lower daytime concentrations by (9.57 ±2.19) μg·m-3 at lakeside stations, with nighttime levels (1.24 ±4.68) μg·m-3 higher.No significant differences were found for PM2.5 concentrations.Using a two-dimensional land surface model, we conducted sensitivity experiments to examine the effects of lake presence and lake-to-urban land distribution under different emission scenarios.Simulations indicated that the model accurately reproduced key temperature and pollutant mass concentrations patterns, comparable to more complex mesoscale model results.Thermal property differences between lake and urban land surfaces significantly impacted low-level atmospheric circulation and vertical stability, altering pollutant diffusion and transport.Daytime thermal stability and limited vertical diffusion over lakeside areas led to a higher concentrations of surface-emitted pollutants near lakes, while concentrations of pollutants formed at mid-levels were lower compared to non-lakeside areas;this pattern reversed at night.Simulation outcomes aligned well with observed data trends and were qualitatively consistent with WRF-Chem results.While emphasizing rigorous monitoring and data processing methods, spatial heterogeneity of urban structures and emissions, as well as observational data limitations, introduced some uncertainties.Future research should incorporate more extensive data from diverse urban regions to better generalize these patterns and strengthen the theoretical foundation for air quality management in urban lakeside environments.
2024,47(6): 949-961, DOI: 10.13878/j.cnki.dqkxxb.20240711011
Abstract:
During the winter season in southern China,freezing rain and ice accretion often lead to substantial socio-economic impacts,including power interruption,tree damage,traffic disruptions,and risk to human safety.Scientific research into freezing rain and ice accretion processes,especially their formation mechanism,is critical for improving the forecast accuracy and mitigating associated hazards.This study presents a detailed numerical investigation of the freezing rain and ice accretion event that occurred in Hubei Province in the winter of 2010.The study utilized the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-Interim reanalysis dataset (0.75°×0.75° spatial resolution,6 hours temporal resolution) as the initial field,in combining with the version v3.7.1 of the weather research and forecasting (WRF) model.Results reveals that the cloud microphysical structure and precipitation formation mechanisms remains consistent across different stages of the ice accretion process,with ice accretion predominantly forming through a supercooled warm rain process.The primary growth mechanism for rainwater in the cloud is the auto-conversion of cloud water to rain and the coalescence of rain with cloud water.Notably,the coalescence of rain with supercooled cloud water plays a more significant role in increasing rainwater mixing ratios.By analyzing the meteorological field and cloud microphysical data from the WRF simulation,and integrate them with the Jones Ice Thickness Prediction Model,the study captures the temporal evolution of ice thickness during the freezing rain event.The Jones model,which utilized readily available physical quantities such as wind speed,precipitation amount,and liquid water content,effectively simulate the trend of ice thickness overtime.A comparative analysis with observed datasets confirms that the model reasonably predict the trend of ice accretion thickness during the event.Meteorological factors,such as air temperature and precipitation intensity,significantly influence ice thickness.For instance,lower air temperature accelerate ice accretion growth,while increased precipitation intensity contributes to rapid ice accumulation.The in-depth analysis of influencing factors provides a robust numerical simulation framework for improving early warnings of freezing rain and ice accretion events.Findings of this study provide a relevant analysis of the physical mechanisms,simulation outcomes,and enhance forecasting capabilities for freezing rain and ice accretion processes.Besides,the results may contribute to advancing forecast accuracy,mitigating socio-economic impacts,and bolstering disaster prevention and response strategies.
During the winter season in southern China,freezing rain and ice accretion often lead to substantial socio-economic impacts,including power interruption,tree damage,traffic disruptions,and risk to human safety.Scientific research into freezing rain and ice accretion processes,especially their formation mechanism,is critical for improving the forecast accuracy and mitigating associated hazards.This study presents a detailed numerical investigation of the freezing rain and ice accretion event that occurred in Hubei Province in the winter of 2010.The study utilized the European Centre for Medium-range Weather Forecasts (ECMWF) ERA-Interim reanalysis dataset (0.75°×0.75° spatial resolution,6 hours temporal resolution) as the initial field,in combining with the version v3.7.1 of the weather research and forecasting (WRF) model.Results reveals that the cloud microphysical structure and precipitation formation mechanisms remains consistent across different stages of the ice accretion process,with ice accretion predominantly forming through a supercooled warm rain process.The primary growth mechanism for rainwater in the cloud is the auto-conversion of cloud water to rain and the coalescence of rain with cloud water.Notably,the coalescence of rain with supercooled cloud water plays a more significant role in increasing rainwater mixing ratios.By analyzing the meteorological field and cloud microphysical data from the WRF simulation,and integrate them with the Jones Ice Thickness Prediction Model,the study captures the temporal evolution of ice thickness during the freezing rain event.The Jones model,which utilized readily available physical quantities such as wind speed,precipitation amount,and liquid water content,effectively simulate the trend of ice thickness overtime.A comparative analysis with observed datasets confirms that the model reasonably predict the trend of ice accretion thickness during the event.Meteorological factors,such as air temperature and precipitation intensity,significantly influence ice thickness.For instance,lower air temperature accelerate ice accretion growth,while increased precipitation intensity contributes to rapid ice accumulation.The in-depth analysis of influencing factors provides a robust numerical simulation framework for improving early warnings of freezing rain and ice accretion events.Findings of this study provide a relevant analysis of the physical mechanisms,simulation outcomes,and enhance forecasting capabilities for freezing rain and ice accretion processes.Besides,the results may contribute to advancing forecast accuracy,mitigating socio-economic impacts,and bolstering disaster prevention and response strategies.
PAN Liujie, DAI Kan, ZHANG Hongfang, QI Chunjuan, LIANG Mian, LIU Jiahuimin, DAI Changming, LI Peirong, SHEN Jiaojiao
2024,47(6): 962-975, DOI: 10.13878/j.cnki.dqkxxb.20231207001
Abstract:
The traditional dichotomous contingency table test, which evaluates the objective performance of numerical weather prediction (NWP) based on the point-to-point matching between forecasted and observed events, has notable limitation when applied to high-resolution NWP or convection-allowing models (CAM). The neighborhood method addresses these limitations by relaxing the grid scale matching constraints between forecasted and observed events, making it particularly valuable for evaluating high-resolution numerical weather forecasts and the post-processing of objective probability forecasts. This paper systematically reviews the key applications of the neighborhood method in weather forecasting, focusing on two key aspects: one is the verification of high-resolution numerical models using neighborhood method; and other is the neighborhood probability or neighborhood probability of ensemble forecasts. First, the study outlines the verification frameworks of two neighborhood methods,“one-to-many” and “many-to-many”, and discusses the data processing techniques associated with the neighborhood method, alongside the physical interpretation of common scoring matrices such as FBS (fractions brier score) and FSS (fractions skill score). It is concluded that, in addition to traditional dichotomous contingency table-based verification metrics, the neighborhood method facilitates comparisons of forecast performance across multiple spatial and temporal scales. This enables the derivation of diagnostic metrics for NWP forecast performance based on scale changes, providing unique advantages. Second, it summarizes the fundamental concepts and statistical meaning of the grid scale neighborhood probability and the neighborhood probability at scales larger than the grid. Discussion focuses on expounding the algorithm workflow and internal meaning of neighborhood ensemble probability (NEP) forecast and neighborhood maximum ensemble probability (NMEP) forecast derived from ensemble forecasts. Third, by examining typical application cases, it analyzes the advantages, disadvantages and applicability of the neighborhood method and neighborhood ensemble probability. Results show that both NEP and NMEP enhance precipitation forecast scores. NEP performs better for large-scale and systematic precipitation forecasts, whereas NMEP is more effective for convective and extreme precipitation events. However, the selection of an appropriate neighborhood radius remains a critical technical challenge, as it is influenced by variations in underlying surface conditions and the optimal neighborhood scales of different NWP products. Finally, the paper discusses future directions for the application of the neighborhood method in weather forecasting. Promising areas of research and application include integrating neighborhood ensemble probability with the temporal dimension, developing metrics for the rare-event ensemble neighborhood probability, and exploring synergies between the neighborhood method and artificial intelligence. These directions hold significant potential for advancing the utility and impact of the neighborhood method in weather forecasting.
The traditional dichotomous contingency table test, which evaluates the objective performance of numerical weather prediction (NWP) based on the point-to-point matching between forecasted and observed events, has notable limitation when applied to high-resolution NWP or convection-allowing models (CAM). The neighborhood method addresses these limitations by relaxing the grid scale matching constraints between forecasted and observed events, making it particularly valuable for evaluating high-resolution numerical weather forecasts and the post-processing of objective probability forecasts. This paper systematically reviews the key applications of the neighborhood method in weather forecasting, focusing on two key aspects: one is the verification of high-resolution numerical models using neighborhood method; and other is the neighborhood probability or neighborhood probability of ensemble forecasts. First, the study outlines the verification frameworks of two neighborhood methods,“one-to-many” and “many-to-many”, and discusses the data processing techniques associated with the neighborhood method, alongside the physical interpretation of common scoring matrices such as FBS (fractions brier score) and FSS (fractions skill score). It is concluded that, in addition to traditional dichotomous contingency table-based verification metrics, the neighborhood method facilitates comparisons of forecast performance across multiple spatial and temporal scales. This enables the derivation of diagnostic metrics for NWP forecast performance based on scale changes, providing unique advantages. Second, it summarizes the fundamental concepts and statistical meaning of the grid scale neighborhood probability and the neighborhood probability at scales larger than the grid. Discussion focuses on expounding the algorithm workflow and internal meaning of neighborhood ensemble probability (NEP) forecast and neighborhood maximum ensemble probability (NMEP) forecast derived from ensemble forecasts. Third, by examining typical application cases, it analyzes the advantages, disadvantages and applicability of the neighborhood method and neighborhood ensemble probability. Results show that both NEP and NMEP enhance precipitation forecast scores. NEP performs better for large-scale and systematic precipitation forecasts, whereas NMEP is more effective for convective and extreme precipitation events. However, the selection of an appropriate neighborhood radius remains a critical technical challenge, as it is influenced by variations in underlying surface conditions and the optimal neighborhood scales of different NWP products. Finally, the paper discusses future directions for the application of the neighborhood method in weather forecasting. Promising areas of research and application include integrating neighborhood ensemble probability with the temporal dimension, developing metrics for the rare-event ensemble neighborhood probability, and exploring synergies between the neighborhood method and artificial intelligence. These directions hold significant potential for advancing the utility and impact of the neighborhood method in weather forecasting.
2024,47(6): 976-992, DOI: 10.13878/j.cnki.dqkxxb.20240620002
Abstract:
Carbon dioxide (CO2) is a primary greenhouse gas,and its rising atmospheric concentration is a critical driver of climate change,contributing to extreme weather,rising sea levels,and ecosystem alterations.Remote sensing via carbon satellites provides a powerful approach for large-scale,precise CO2 monitoring;however,challenges with spatial and temporal sparsity limit the accuracy and continuity of CO2 concentration (XCO2) estimates,particularly across large regions like China's Yangtze River delta.To address these limitations,this study introduces the Space-Time Soft Attention Network (ST-SAN),a novel model designed to enhance the spatiotemporal resolution of XCO2 estimates derived from carbon satellite data.The model leverages multi-source datasets (including human activity,meteorological,and vegetation) alongside carbon satellite observations,achieving a seamless XCO2 dataset with a 0.05° spatial resolution,thus providing a detailed view of regional CO2 dynamics.Training the ST-SAN model on data from 2016 to 2020,the methodology employs soft attention mechanisms to prioritize relevant features across spatial and temporal dimensions,enabling more accurate XCO2 predictions.The model's effectiveness was rigorously evaluated by comparing reconstructed XCO2 data with observations from the Orbiting Carbon Observatory-2 and ground-based monitoring stations,demonstrating high consistency and reliability.By integrating diverse datasets,the ST-SAN model effectively addresses the sparsity issues in satellite observations,enhancing predictive performance and offering a comprehensive framework for high-resolution CO2 estimation.These findings underscore the potential of advanced machine learning techniques to improve atmospheric monitoring and provide critical insights for climate mitigation efforts.Future research could refine this model with additional data sources,extend its applicability to varied regions,and explore long-term CO2 trends to better understand the influences of human activity and natural processes on greenhouse gas emissions.The study not only demonstrates the feasibility of high-resolution XCO2 estimation but also establishes a foundation for more accurate climate assessments and informed environmental policy.
Carbon dioxide (CO2) is a primary greenhouse gas,and its rising atmospheric concentration is a critical driver of climate change,contributing to extreme weather,rising sea levels,and ecosystem alterations.Remote sensing via carbon satellites provides a powerful approach for large-scale,precise CO2 monitoring;however,challenges with spatial and temporal sparsity limit the accuracy and continuity of CO2 concentration (XCO2) estimates,particularly across large regions like China's Yangtze River delta.To address these limitations,this study introduces the Space-Time Soft Attention Network (ST-SAN),a novel model designed to enhance the spatiotemporal resolution of XCO2 estimates derived from carbon satellite data.The model leverages multi-source datasets (including human activity,meteorological,and vegetation) alongside carbon satellite observations,achieving a seamless XCO2 dataset with a 0.05° spatial resolution,thus providing a detailed view of regional CO2 dynamics.Training the ST-SAN model on data from 2016 to 2020,the methodology employs soft attention mechanisms to prioritize relevant features across spatial and temporal dimensions,enabling more accurate XCO2 predictions.The model's effectiveness was rigorously evaluated by comparing reconstructed XCO2 data with observations from the Orbiting Carbon Observatory-2 and ground-based monitoring stations,demonstrating high consistency and reliability.By integrating diverse datasets,the ST-SAN model effectively addresses the sparsity issues in satellite observations,enhancing predictive performance and offering a comprehensive framework for high-resolution CO2 estimation.These findings underscore the potential of advanced machine learning techniques to improve atmospheric monitoring and provide critical insights for climate mitigation efforts.Future research could refine this model with additional data sources,extend its applicability to varied regions,and explore long-term CO2 trends to better understand the influences of human activity and natural processes on greenhouse gas emissions.The study not only demonstrates the feasibility of high-resolution XCO2 estimation but also establishes a foundation for more accurate climate assessments and informed environmental policy.
2024,47(6): 993-1003, DOI: 10.13878/j.cnki.dqkxxb.20231016001
Abstract:
In recent years,global warming has intensified extreme weather and climate events,significantly impacting urban areas.Shanghai,located in the eastern coastal region of China,is increasingly vulnerable to weather-related disasters.As a megacity,its urban operations are heavily influenced by these events,particularly gales,which pose severe threats to both infrastructure and public safety.Despite their impact,research on gales in Shanghai has been limited by incomplete and inconsistent disaster data.Since 2007,the Shanghai Emergency Response and Coordination Center has shared weather-related alarm data with the Shanghai Center for Meteorological Disaster Prevention Technology.This data includes detailed information on the timing,location,and nature of disasters,enabling a more thorough analysis of the impacts of weather events on the city.Using hourly maximum wind speed data from Shanghai's automatic stations and real-time gale disaster data provided by the Shanghai Emergency Response Coordination Center from 2008 to 2019,this study analyzes the spatiotemporal distributions of urban gales and their correlations with disasters.The findings are as follows:1) Gales and disasters exhibit significant seasonal variations,with force 8 gale days,maximum wind speeds,and disaster occurrences all peaking in summer.Summer gale days account for 41％ of the year,and gale-related disasters constitute over 80％ of the total.Inter-annual fluctuations in disasters are pronounced,primarily influenced by extreme weather events.2) Differences in gales and disasters are evident between urban and suburban areas:Ⅰ) Wind speeds are notably lower in downtown regions due to urbanization,while Chongming Island,Hengsha Island,and coastal areas southeast of Pudong experience significantly higher wind speeds due to funnel-shaped terrain effects.Ⅱ) The density of disaster cases is highest in the central city (up to 37 cases／km2) and remains relatively high in regional centers and sub-centers,with the number of disasters positively correlated with population density and GDP.III) Housing-related damages are most prominent in downtown areas and their surroundings,while damages to trees and power lines are significant in Chongming District,and vehicle-related damages are prevalent in Jiading District and Songjiang District.3) Gale disasters in Shanghai can be categorized into four types:Jiang-Huai cyclone gales,thunderstorm gales,tropical cyclone gales,and cold air gales.Thunderstorm gales are the most frequent,while tropical cyclone gales are the most severe.Jiang-Huai cyclone gales impact a broad area of the city but are infrequent,and cold air gales are the least severe.4) Trees and vehicles are the most common disaster-affected entities in strong wind events,followed by power lines and rain shelters.Gale disasters often trigger chain reactions.To effectively prevent and mitigate gale-related disasters,it is crucial for the city to develop detailed emergency response plans and enhance cooperation among management departments.Future research should focus on establishing thresholds for gale disasters and creating risk warning products,thereby advancing the city's disaster management capabilities to a more sophisticated and efficient level.
In recent years,global warming has intensified extreme weather and climate events,significantly impacting urban areas.Shanghai,located in the eastern coastal region of China,is increasingly vulnerable to weather-related disasters.As a megacity,its urban operations are heavily influenced by these events,particularly gales,which pose severe threats to both infrastructure and public safety.Despite their impact,research on gales in Shanghai has been limited by incomplete and inconsistent disaster data.Since 2007,the Shanghai Emergency Response and Coordination Center has shared weather-related alarm data with the Shanghai Center for Meteorological Disaster Prevention Technology.This data includes detailed information on the timing,location,and nature of disasters,enabling a more thorough analysis of the impacts of weather events on the city.Using hourly maximum wind speed data from Shanghai's automatic stations and real-time gale disaster data provided by the Shanghai Emergency Response Coordination Center from 2008 to 2019,this study analyzes the spatiotemporal distributions of urban gales and their correlations with disasters.The findings are as follows:1) Gales and disasters exhibit significant seasonal variations,with force 8 gale days,maximum wind speeds,and disaster occurrences all peaking in summer.Summer gale days account for 41％ of the year,and gale-related disasters constitute over 80％ of the total.Inter-annual fluctuations in disasters are pronounced,primarily influenced by extreme weather events.2) Differences in gales and disasters are evident between urban and suburban areas:Ⅰ) Wind speeds are notably lower in downtown regions due to urbanization,while Chongming Island,Hengsha Island,and coastal areas southeast of Pudong experience significantly higher wind speeds due to funnel-shaped terrain effects.Ⅱ) The density of disaster cases is highest in the central city (up to 37 cases／km2) and remains relatively high in regional centers and sub-centers,with the number of disasters positively correlated with population density and GDP.III) Housing-related damages are most prominent in downtown areas and their surroundings,while damages to trees and power lines are significant in Chongming District,and vehicle-related damages are prevalent in Jiading District and Songjiang District.3) Gale disasters in Shanghai can be categorized into four types:Jiang-Huai cyclone gales,thunderstorm gales,tropical cyclone gales,and cold air gales.Thunderstorm gales are the most frequent,while tropical cyclone gales are the most severe.Jiang-Huai cyclone gales impact a broad area of the city but are infrequent,and cold air gales are the least severe.4) Trees and vehicles are the most common disaster-affected entities in strong wind events,followed by power lines and rain shelters.Gale disasters often trigger chain reactions.To effectively prevent and mitigate gale-related disasters,it is crucial for the city to develop detailed emergency response plans and enhance cooperation among management departments.Future research should focus on establishing thresholds for gale disasters and creating risk warning products,thereby advancing the city's disaster management capabilities to a more sophisticated and efficient level.
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在线获得:May 21, 2024, DOI: 10.13878/j.cnki.dqkxxb.20231129002
Abstract:
Lakes played an important role in regulating local climate and air quality. The impact of urban lakes on urban local atmospheric boundary layer circulation and atmospheric pollutant transport mechanisms was an important issue in current urban air quality research. This study analyzed the diurnal variation characteristics of near-surface atmospheric pollutant concentrations at lakeside stations in urban Nanjing in summer, and compared them with observations from non-lakeside urban stations relatively far away from the lake. The results showed that for NO2, which was mainly emitted near the ground, the average concentration in lakeside stations was about 1.64±0.29 μg·m-3 higher than that in non-lakeside city stations during the day, and was about 0.51±1.39 μg·m-3 lower at night. O3, which was mainly generated and transported in the middle and high levels of the boundary layer, showed the opposite trend: that is, the average concentration of lakeside stations during the day was about 9.57±2.19 μg·m-3 lower than that of non-lakeside city stations, while it was about 1.24±4.68 μg·m-3 higher at night; No similar concentration differences were found in PM2.5. We further used a two-dimensional land surface process model to conduct numerical experiments on the sensitivity of lake land use to lake-urban land distribution and different emission scenarios. The simulation results showed that the difference in thermal properties between lake surfaces and land surfaces in urban areas affects low-altitude atmospheric circulation. and vertical thermal stability, and further affected the diffusion and transmission process of urban near-surface air pollution concentration. Affected by the existence of the lake, the thermal stability of the vertical atmosphere above the lake and lakeside area during the day was higher than that over the non-lakeside area, and the vertical diffusion of the lower atmosphere was weaker, resulting in higher concentrations of pollutants mainly emitted near the surface in the lakeside area. The concentration of pollutants mainly emitted and generated at mid- and high-altitudes was low, while the situation was opposite at night. The simulation results were consistent with the observed data trends and qualitatively consistent with the WRF-Chem simulation results.
Lakes played an important role in regulating local climate and air quality. The impact of urban lakes on urban local atmospheric boundary layer circulation and atmospheric pollutant transport mechanisms was an important issue in current urban air quality research. This study analyzed the diurnal variation characteristics of near-surface atmospheric pollutant concentrations at lakeside stations in urban Nanjing in summer, and compared them with observations from non-lakeside urban stations relatively far away from the lake. The results showed that for NO2, which was mainly emitted near the ground, the average concentration in lakeside stations was about 1.64±0.29 μg·m-3 higher than that in non-lakeside city stations during the day, and was about 0.51±1.39 μg·m-3 lower at night. O3, which was mainly generated and transported in the middle and high levels of the boundary layer, showed the opposite trend: that is, the average concentration of lakeside stations during the day was about 9.57±2.19 μg·m-3 lower than that of non-lakeside city stations, while it was about 1.24±4.68 μg·m-3 higher at night; No similar concentration differences were found in PM2.5. We further used a two-dimensional land surface process model to conduct numerical experiments on the sensitivity of lake land use to lake-urban land distribution and different emission scenarios. The simulation results showed that the difference in thermal properties between lake surfaces and land surfaces in urban areas affects low-altitude atmospheric circulation. and vertical thermal stability, and further affected the diffusion and transmission process of urban near-surface air pollution concentration. Affected by the existence of the lake, the thermal stability of the vertical atmosphere above the lake and lakeside area during the day was higher than that over the non-lakeside area, and the vertical diffusion of the lower atmosphere was weaker, resulting in higher concentrations of pollutants mainly emitted near the surface in the lakeside area. The concentration of pollutants mainly emitted and generated at mid- and high-altitudes was low, while the situation was opposite at night. The simulation results were consistent with the observed data trends and qualitatively consistent with the WRF-Chem simulation results.
在线获得:May 21, 2024, DOI: DOI: 10.13878/j.cnki.dqkxxb.20230518003
Abstract:
Based on the reanalysis data of ERA5 and the historical output data of MPI-ESM1-2-HR model in CMIP6, the relationship between the inter-hemispheric atmospheric mass oscillation (IHO) and East Asian winter monsoon (EAWM) and its effect on winter temperature in China are verified. The results of reanalysis and model indicate that there is a significant positive correlation between winter IHO and East Asian winter monsoon. The IHO is closely related to the EAWM through the redistribution of global atmospheric mass. When the IHO is positive phase, the atmospheric mass accumulates abnormally in the northern part of Eurasia, but loses abnormally in the middle and low latitudes, which makes the ocean-land pressure difference in East Asia increase significantly, and the EAWM intensification. At the same time, it also significant cooling the winter surface temperature in central China, and vice versa. Further analysis shows that the temperature in the tropical lower stratosphere can affect the atmospheric temperature in the Antarctic troposphere by regulating the meridional distribution of ozone content through the residual circulation, thus playing a major driving role in the interannual variation of IHO.
Based on the reanalysis data of ERA5 and the historical output data of MPI-ESM1-2-HR model in CMIP6, the relationship between the inter-hemispheric atmospheric mass oscillation (IHO) and East Asian winter monsoon (EAWM) and its effect on winter temperature in China are verified. The results of reanalysis and model indicate that there is a significant positive correlation between winter IHO and East Asian winter monsoon. The IHO is closely related to the EAWM through the redistribution of global atmospheric mass. When the IHO is positive phase, the atmospheric mass accumulates abnormally in the northern part of Eurasia, but loses abnormally in the middle and low latitudes, which makes the ocean-land pressure difference in East Asia increase significantly, and the EAWM intensification. At the same time, it also significant cooling the winter surface temperature in central China, and vice versa. Further analysis shows that the temperature in the tropical lower stratosphere can affect the atmospheric temperature in the Antarctic troposphere by regulating the meridional distribution of ozone content through the residual circulation, thus playing a major driving role in the interannual variation of IHO.
在线获得:March 06, 2024, DOI: 10.13878/j.cnki.dqkxxb.20240212001
Abstract:
In the field of artificial precipitation enhancement, aircraft observation is still the only effective ways to directly obtain the phase state of cloud particles. Because the liquid supercooled water content in the cloud is an important parameter to judge the microphysical structure characteristics of the cloud system, based on the layered vertical detection data of Par-ticle Measurement System (PMS) in the Sanjiangyuan National Nature Reserve, we analyzed the microphysical charac-teristics of a typical layered cloud system in this area in spring. The results show that: (1) the typical stratiform cloud system in spring cloud system is mainly composed of three layers, the uppermost Cs (cirrostratus) is ice-phase cloud, and the AS (altostratus) in upper layer (high-level cloud) and the lower layer are mixed clouds. Moreover, the high super-cooled water area is mainly distributed in the middle, upper and lower parts of the low-level As, where the cloud particle concentration and supercooled water content are the highest, with obvious regional characteristics. (2) The liquid cloud particles in the As layer are concentrated in the median diameter range of 3.5 ~ 27.5 μm, and the cloud particles larger than 30.5 μm are ice-phase. In the middle and upper part of the low As layer, there is an obvious growth phenomenon in the high supercooled water area, and there is obvious ice crystals effect in the middle and lower part. (3) In the low su-percooled water area, the average content ratio of supercooled water is 90.8±10.9%, and the middle to lower parts (95.6±5.6%) is significantly higher than the middle and upper parts (79.8±12.1%). The accurate understanding and judgment of the microphysical structure of the layered cloud system in Sanjiangyuan will provide a reliable observation basis for the catalysis of artificial precipitation enhancement in this area.
In the field of artificial precipitation enhancement, aircraft observation is still the only effective ways to directly obtain the phase state of cloud particles. Because the liquid supercooled water content in the cloud is an important parameter to judge the microphysical structure characteristics of the cloud system, based on the layered vertical detection data of Par-ticle Measurement System (PMS) in the Sanjiangyuan National Nature Reserve, we analyzed the microphysical charac-teristics of a typical layered cloud system in this area in spring. The results show that: (1) the typical stratiform cloud system in spring cloud system is mainly composed of three layers, the uppermost Cs (cirrostratus) is ice-phase cloud, and the AS (altostratus) in upper layer (high-level cloud) and the lower layer are mixed clouds. Moreover, the high super-cooled water area is mainly distributed in the middle, upper and lower parts of the low-level As, where the cloud particle concentration and supercooled water content are the highest, with obvious regional characteristics. (2) The liquid cloud particles in the As layer are concentrated in the median diameter range of 3.5 ~ 27.5 μm, and the cloud particles larger than 30.5 μm are ice-phase. In the middle and upper part of the low As layer, there is an obvious growth phenomenon in the high supercooled water area, and there is obvious ice crystals effect in the middle and lower part. (3) In the low su-percooled water area, the average content ratio of supercooled water is 90.8±10.9%, and the middle to lower parts (95.6±5.6%) is significantly higher than the middle and upper parts (79.8±12.1%). The accurate understanding and judgment of the microphysical structure of the layered cloud system in Sanjiangyuan will provide a reliable observation basis for the catalysis of artificial precipitation enhancement in this area.
Zhou Xiaotian, Zhang Qianru, Guo Qingyan, Chen Yiling, Zhou Xuesong, Li Changjun, Feng Yong, Zhang Ping
在线获得:November 17, 2023, DOI: 10.13878/j.cnki.dqkxxb.20221130001
Abstract:
As the most basic physical quantity for studying on climate evolution, the integrity and accuracy of daily temperature series are of great significance for climate analysis and assessment. In recent years, with the deployment of a large number of unmanned ground intensified automatic weather stations, missing data with double random characteristics such as random distribution of stations and random lengths of series, which poses significant obstacles to climate analysis and operational applications. In view of the shortcomings of the existing methods for meteorological data interpolation, a new twice interpolation method of daily temperature data based on dynamic time warping (DTW) is proposed in this paper. The method adopts a real-time interpolation strategy, which mainly includes: (1) The method decomposes the temperature observation time series into a fitted straight line and a residual curved line by using univariate linear regression equation, and recomposes new temperature series by combining the two lines; (2) The method provides the definition and interpolation conditions of temperature interpolation areas; (3)The method proposes a new model for calculating the distance between stations by DTW . The collecting temperature data from Shandong Province in 2021 is used to test the method, and the test results show that the method can meet the interpolation needs of daily temperature data with double random characteristics, and the combination method of DTW distance and twice interpolation in the interpolation process can achieve a better effect than any of the other combination based on site geographical proximity relationships; the method is sensitive to terrain, and the interpolation effect in plain or hilly area is better than that in mountainous area.
As the most basic physical quantity for studying on climate evolution, the integrity and accuracy of daily temperature series are of great significance for climate analysis and assessment. In recent years, with the deployment of a large number of unmanned ground intensified automatic weather stations, missing data with double random characteristics such as random distribution of stations and random lengths of series, which poses significant obstacles to climate analysis and operational applications. In view of the shortcomings of the existing methods for meteorological data interpolation, a new twice interpolation method of daily temperature data based on dynamic time warping (DTW) is proposed in this paper. The method adopts a real-time interpolation strategy, which mainly includes: (1) The method decomposes the temperature observation time series into a fitted straight line and a residual curved line by using univariate linear regression equation, and recomposes new temperature series by combining the two lines; (2) The method provides the definition and interpolation conditions of temperature interpolation areas; (3)The method proposes a new model for calculating the distance between stations by DTW . The collecting temperature data from Shandong Province in 2021 is used to test the method, and the test results show that the method can meet the interpolation needs of daily temperature data with double random characteristics, and the combination method of DTW distance and twice interpolation in the interpolation process can achieve a better effect than any of the other combination based on site geographical proximity relationships; the method is sensitive to terrain, and the interpolation effect in plain or hilly area is better than that in mountainous area.
在线获得:November 01, 2023, DOI: 10.13878/j.cnki.dqkxxb.20231016001
Abstract:
Based on the hourly maximum wind speed data of Shanghai automatic stations and real-time gale disaster data provided by Shanghai Emergency Response Coordination Center from 2008 to 2019,?characteristics of spatiotemporal distributions and correlations between urban gales and disasters are analyzed in order to explore the impacts of strong winds in Shanghai. The results are as follows: (1) The gales and disasters present significant seasonal variations with force 8 gale days, maximum wind speeds, and disasters all exhibiting a summer single peak pattern. Summer gale days account for 41% of the year, and gale disasters account for over 80% of the year. The inter-annual fluctuations of disasters are significant, mainly influenced by extreme weathers. (2)The gales and disasters show significant differences between urban and suburban areas: Ⅰ) Wind speeds in downtown areas are much lower due to the impact of urbanization. Ⅱ) The density of disaster cases ranks the highest in central city (up to 37 cases/km2) and relatively high in regional centers and sub-centers. It is also found that the number of disasters in each region is positively correlated with its population and GDP. III) The housing related damages are relatively prominent in downtown and its surrounding regions, but the least significant in Chongming District. (3) The disastrous gale weather in Shanghai can be divided into four categories: Jiang-huai cyclone gales, thunderstorm gales, tropical cyclone gales and cold air gales, with thunderstorm gales the most frequent and tropical cyclone gales the most severe. (4) Trees and vehicles are the most common disaster-bearing bodies of strong wind disasters in Shanghai, followed by power lines and rain shelters. Gales cause disasters with obvious chain reactions. The study on the characteristics and impacts of urban gales are of great significance for the gale risk warning development and disaster prevention and mitigation.
Based on the hourly maximum wind speed data of Shanghai automatic stations and real-time gale disaster data provided by Shanghai Emergency Response Coordination Center from 2008 to 2019,?characteristics of spatiotemporal distributions and correlations between urban gales and disasters are analyzed in order to explore the impacts of strong winds in Shanghai. The results are as follows: (1) The gales and disasters present significant seasonal variations with force 8 gale days, maximum wind speeds, and disasters all exhibiting a summer single peak pattern. Summer gale days account for 41% of the year, and gale disasters account for over 80% of the year. The inter-annual fluctuations of disasters are significant, mainly influenced by extreme weathers. (2)The gales and disasters show significant differences between urban and suburban areas: Ⅰ) Wind speeds in downtown areas are much lower due to the impact of urbanization. Ⅱ) The density of disaster cases ranks the highest in central city (up to 37 cases/km2) and relatively high in regional centers and sub-centers. It is also found that the number of disasters in each region is positively correlated with its population and GDP. III) The housing related damages are relatively prominent in downtown and its surrounding regions, but the least significant in Chongming District. (3) The disastrous gale weather in Shanghai can be divided into four categories: Jiang-huai cyclone gales, thunderstorm gales, tropical cyclone gales and cold air gales, with thunderstorm gales the most frequent and tropical cyclone gales the most severe. (4) Trees and vehicles are the most common disaster-bearing bodies of strong wind disasters in Shanghai, followed by power lines and rain shelters. Gales cause disasters with obvious chain reactions. The study on the characteristics and impacts of urban gales are of great significance for the gale risk warning development and disaster prevention and mitigation.
在线获得:May 08, 2023, DOI: 10.13878/j.cnki.dqkxxb.20230501007
Abstract:
From March to April in 2023, 9 dust weather processes occurred in China.The processes from 19 to 24 March and from 9 to 13 April reached the level of strong sandstorm and sandstorm respectively, bringing adverse impacts on population health, ecological envi-ronment and transportation.The anomalous Eurasian atmospheric circulations showed a zonal distribution from the late winter to early spring, the Siberian high and the East Asian trough were weak, and the area around Mongolia was controlled by the high pressure anom-aly, which resulted in the continuous warm surface air temperature and soil temperature in the dust source area (the second highest since 1980).In addition, the lower troposphere also lacked water vapor transporting to the sand source, resulting in significantly less precipita-tion in the winter and spring of 2023 (the lowest in history since 1980).The continuous warm and dry climate conditions in winter and spring resulted in a large number of dry and loose gravel in the dust source area from February to March in 2023, which provided ex-tremely favorable dust source conditions for the occurrence of sandstorms.The synoptic disturbance systems that triggered the sandstorms in March 19—24 and April 9—13 were both Mongolian cyclones.However, the two Mongolian cyclones and their configuration with the rear cold high were different, which directly caused the differences in the intensity, path and duration of the two sandstorms.From 19 to 24 March, the dust process reached the level of strong sandstorm, causing PM10 concentrations in Beijing and Harbin to exceed 2000 and 3600 μg·m?3 respectively.The sandstorm from 9 to 13 April not only affected Northeast and North China, but also caused a significant increase in PM10 concentration in cities south of the Yangtze River, such as Hangzhou and Fuzhou.Moreover, the research emphases of dust events from the aspects of climate cumulative effect and synoptic disturbance mechanism are analyzed, and the necessity and key points of conducting subseasonal-seasonal and interdecadal prediction of dust events are discussed.Furthermore, severe sandstorms in 2021 and 2023 also raise the scientific question, "Have dust events entered a new period of activity?".To answer this question, it is neces-sary to study the interdecadal mechanism and prediction of dust change, and also to predict the future change of dust event trend to a cer-tain extent.
From March to April in 2023, 9 dust weather processes occurred in China.The processes from 19 to 24 March and from 9 to 13 April reached the level of strong sandstorm and sandstorm respectively, bringing adverse impacts on population health, ecological envi-ronment and transportation.The anomalous Eurasian atmospheric circulations showed a zonal distribution from the late winter to early spring, the Siberian high and the East Asian trough were weak, and the area around Mongolia was controlled by the high pressure anom-aly, which resulted in the continuous warm surface air temperature and soil temperature in the dust source area (the second highest since 1980).In addition, the lower troposphere also lacked water vapor transporting to the sand source, resulting in significantly less precipita-tion in the winter and spring of 2023 (the lowest in history since 1980).The continuous warm and dry climate conditions in winter and spring resulted in a large number of dry and loose gravel in the dust source area from February to March in 2023, which provided ex-tremely favorable dust source conditions for the occurrence of sandstorms.The synoptic disturbance systems that triggered the sandstorms in March 19—24 and April 9—13 were both Mongolian cyclones.However, the two Mongolian cyclones and their configuration with the rear cold high were different, which directly caused the differences in the intensity, path and duration of the two sandstorms.From 19 to 24 March, the dust process reached the level of strong sandstorm, causing PM10 concentrations in Beijing and Harbin to exceed 2000 and 3600 μg·m?3 respectively.The sandstorm from 9 to 13 April not only affected Northeast and North China, but also caused a significant increase in PM10 concentration in cities south of the Yangtze River, such as Hangzhou and Fuzhou.Moreover, the research emphases of dust events from the aspects of climate cumulative effect and synoptic disturbance mechanism are analyzed, and the necessity and key points of conducting subseasonal-seasonal and interdecadal prediction of dust events are discussed.Furthermore, severe sandstorms in 2021 and 2023 also raise the scientific question, "Have dust events entered a new period of activity?".To answer this question, it is neces-sary to study the interdecadal mechanism and prediction of dust change, and also to predict the future change of dust event trend to a cer-tain extent.
在线获得:April 06, 2023, DOI: 10.13878/j.cnki.dqkxxb.20220805002
Abstract:
Southeast Asia has a complex topography and a large population. The response of extreme precipitation events to global warming is sensitive and its future evolutionary characteristics are widely valued. In this study, 26 global climate models (GCMs) are used to present projected change in precipitation extremes over Southeast Asia at the end of 21st century based on the Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. The thermodynamic and dynamic contributions to future changes in precipitation are analyzed by decomposition of moisture budget equation. The multi-model ensemble medians show that, compared with the historical reference period (1985—2014), the frequency and intensity of climatological precipitation and precipitation extremes in most of Southeast Asia are obviously increased at the end of 21st century (2070—2099) under SSP2-4.5 and SSP5-8.5 scenarios. The variation of precipitation extremes shows significant spatial differences under the global warming. Precipitation extremes with short duration and high intensity will occur in the Kalimantan. In southern Sumatra, the total precipitation of wet days (PRCPTOT) tends to decrease significantly and the occurrence of consecutive dry days (CDD) become more frequent. Except for heavy precipitation days (R10mm), the change of extreme precipitation indices with more pronounced magnitudes under SSP5-8.5 scenario than SSP2-4.5 scenario. The contribution rate of heavy precipitation (R95pTOT) increased by 22% (41%) under SSP2-4.5 (SSP5-8.5) scenario. Quantitative analysis of moisture budget equation shows that, thermodynamic and dynamic effects mainly result in the precipitation climatological changes at the end of 21st century. Compared with the large-scale circulation changes, the thermodynamic component with higher inter-model consistency is the main contribution term. Under SSP2-4.5 (SSP5-8.5) scenario, the contribution of thermodynamic effects accounts for 65% (64%) of the P-E (precipitation minus evaporation) changes. However, the dynamic effects show a counteracting trend to the changes of P-E, which contributes 35% (36%). Moisture convergence caused by atmospheric specific humidity changes is considered as the dominating factor of projected precipitation increase.
Southeast Asia has a complex topography and a large population. The response of extreme precipitation events to global warming is sensitive and its future evolutionary characteristics are widely valued. In this study, 26 global climate models (GCMs) are used to present projected change in precipitation extremes over Southeast Asia at the end of 21st century based on the Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. The thermodynamic and dynamic contributions to future changes in precipitation are analyzed by decomposition of moisture budget equation. The multi-model ensemble medians show that, compared with the historical reference period (1985—2014), the frequency and intensity of climatological precipitation and precipitation extremes in most of Southeast Asia are obviously increased at the end of 21st century (2070—2099) under SSP2-4.5 and SSP5-8.5 scenarios. The variation of precipitation extremes shows significant spatial differences under the global warming. Precipitation extremes with short duration and high intensity will occur in the Kalimantan. In southern Sumatra, the total precipitation of wet days (PRCPTOT) tends to decrease significantly and the occurrence of consecutive dry days (CDD) become more frequent. Except for heavy precipitation days (R10mm), the change of extreme precipitation indices with more pronounced magnitudes under SSP5-8.5 scenario than SSP2-4.5 scenario. The contribution rate of heavy precipitation (R95pTOT) increased by 22% (41%) under SSP2-4.5 (SSP5-8.5) scenario. Quantitative analysis of moisture budget equation shows that, thermodynamic and dynamic effects mainly result in the precipitation climatological changes at the end of 21st century. Compared with the large-scale circulation changes, the thermodynamic component with higher inter-model consistency is the main contribution term. Under SSP2-4.5 (SSP5-8.5) scenario, the contribution of thermodynamic effects accounts for 65% (64%) of the P-E (precipitation minus evaporation) changes. However, the dynamic effects show a counteracting trend to the changes of P-E, which contributes 35% (36%). Moisture convergence caused by atmospheric specific humidity changes is considered as the dominating factor of projected precipitation increase.
在线获得:April 06, 2023, DOI: 10.13878/j.cnki.dqkxxb.20220715001
Abstract:
Based on the dataset of precipitation phenomenon instrument in Zhengzhou, the dual polarization radars, the conventional observation and the China’s First Generation Global Atmosphere and Land Reanalysis data, the microphysical characteristics of “7·20” extremely heavy rainfall in Zhengzhou were analyzed. The extremely heavy rainfall was affected by multiscale weather systems, providing favorable environmental conditions for the formation of complex microphysical characteristics. The results showed that the raindrop size distributions changed significantly with time, and values of the raindrop size distribution parameters were widely distributed, covering the distribution area from continental convective precipitation to maritime convective precipitation. The number density of small drops was much higher than that of common convective precipitation in East Asia and the average value in South China in summer, and a large number of large drops also existed, leading to the higher precipitation efficiency during the strongest precipitation period from 16:00 to 17:00 on the 20th July. The low-centroid structure of convective storm was observed by dual polarization radar, showing the typical characteristics of warm cloud. The intense warm-rain processes below the 0℃ layer played an important role in the formation of the extremely heavy rainfall because of the melting of lots of ice particles and high efficiency for the growth of raindrops.
Based on the dataset of precipitation phenomenon instrument in Zhengzhou, the dual polarization radars, the conventional observation and the China’s First Generation Global Atmosphere and Land Reanalysis data, the microphysical characteristics of “7·20” extremely heavy rainfall in Zhengzhou were analyzed. The extremely heavy rainfall was affected by multiscale weather systems, providing favorable environmental conditions for the formation of complex microphysical characteristics. The results showed that the raindrop size distributions changed significantly with time, and values of the raindrop size distribution parameters were widely distributed, covering the distribution area from continental convective precipitation to maritime convective precipitation. The number density of small drops was much higher than that of common convective precipitation in East Asia and the average value in South China in summer, and a large number of large drops also existed, leading to the higher precipitation efficiency during the strongest precipitation period from 16:00 to 17:00 on the 20th July. The low-centroid structure of convective storm was observed by dual polarization radar, showing the typical characteristics of warm cloud. The intense warm-rain processes below the 0℃ layer played an important role in the formation of the extremely heavy rainfall because of the melting of lots of ice particles and high efficiency for the growth of raindrops.
在线获得:April 06, 2023, DOI: 10.13878/j.cnki.dqkxxb.20220715002
Abstract:
In order to understand the causes of summer precipitation anomalies in North China and improve climate monitoring and prediction technology, based on the summer precipitation data in North China and NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data from 1961 to 2021, using the methods of correlation, synthesis and circulation anomaly reconstruction, the circulation background of summer (June -August) precipitation anomaly in North China and the relationship between East Asian subtropical summer monsoon, North China dynamic rise index and summer precipitation in North China are analyzed. The results show that: (1) the circulation conditions of abnormal precipitation in North China in summer are that the high ridge of Ural Mountain at 500hPa in summer is stronger, the Baikal Lake trough is deeper, the subtropical high in the Northwest Pacific is north by west near the Korean Peninsula, and North China is in the circulation situation of "high in the East and low in the west", so the dynamic rising conditions are favorable. At the same time, the 850hPa Indian monsoon is stronger, the East Asian subtropical summer monsoon is significantly stronger, and the water vapor source in North China is sufficient. (2) The East Asian subtropical summer monsoon index and North China dynamic rise index defined in this paper have a good indication of summer precipitation anomalies in North China. When the two indexes are stronger, the summer precipitation in North China is abnormally more. If both indexes are weak, the summer precipitation in North China is abnormally less. If the two indexes are inconsistent, the summer precipitation in North China is basically normal. (3) The abnormally more or less summer rainfall in North China is the result of the synergy of the East Asian subtropical summer monsoon and the dynamic upward movement in North China. The subtropical summer monsoon in East Asia is stronger (the dynamic upward movement in North China is stronger), the Baikal trough in the 500hPa layer is deepened, the subtropical high in the Northwest Pacific is north by west, and North China is under the control of the circulation of "high in the East and low in the West", so the dynamic upward conditions are favorable. At the same time, the Indian summer monsoon at 850hPa and the subtropical summer monsoon in East Asia are relatively strong, which will strengthen the water vapor transmission of the west wind in the tropical Indian Ocean and the south wind in the subtropical region of East Asia, and the water vapor source in North China is sufficient. The above situation will cause abnormally more summer precipitation in North China. On the contrary, the summer precipitation in North China will be abnormally less. (4) From April to May in the early stage, the Baikal trough in the 500hPa layer deepened, the subtropical high in the Northwest Pacific was northerly, and the southerly wind in East Asia in the 850hPa layer was significantly stronger, which can be used as a climate monitoring and prediction index for the abnormally more summer precipitation in North China.
In order to understand the causes of summer precipitation anomalies in North China and improve climate monitoring and prediction technology, based on the summer precipitation data in North China and NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data from 1961 to 2021, using the methods of correlation, synthesis and circulation anomaly reconstruction, the circulation background of summer (June -August) precipitation anomaly in North China and the relationship between East Asian subtropical summer monsoon, North China dynamic rise index and summer precipitation in North China are analyzed. The results show that: (1) the circulation conditions of abnormal precipitation in North China in summer are that the high ridge of Ural Mountain at 500hPa in summer is stronger, the Baikal Lake trough is deeper, the subtropical high in the Northwest Pacific is north by west near the Korean Peninsula, and North China is in the circulation situation of "high in the East and low in the west", so the dynamic rising conditions are favorable. At the same time, the 850hPa Indian monsoon is stronger, the East Asian subtropical summer monsoon is significantly stronger, and the water vapor source in North China is sufficient. (2) The East Asian subtropical summer monsoon index and North China dynamic rise index defined in this paper have a good indication of summer precipitation anomalies in North China. When the two indexes are stronger, the summer precipitation in North China is abnormally more. If both indexes are weak, the summer precipitation in North China is abnormally less. If the two indexes are inconsistent, the summer precipitation in North China is basically normal. (3) The abnormally more or less summer rainfall in North China is the result of the synergy of the East Asian subtropical summer monsoon and the dynamic upward movement in North China. The subtropical summer monsoon in East Asia is stronger (the dynamic upward movement in North China is stronger), the Baikal trough in the 500hPa layer is deepened, the subtropical high in the Northwest Pacific is north by west, and North China is under the control of the circulation of "high in the East and low in the West", so the dynamic upward conditions are favorable. At the same time, the Indian summer monsoon at 850hPa and the subtropical summer monsoon in East Asia are relatively strong, which will strengthen the water vapor transmission of the west wind in the tropical Indian Ocean and the south wind in the subtropical region of East Asia, and the water vapor source in North China is sufficient. The above situation will cause abnormally more summer precipitation in North China. On the contrary, the summer precipitation in North China will be abnormally less. (4) From April to May in the early stage, the Baikal trough in the 500hPa layer deepened, the subtropical high in the Northwest Pacific was northerly, and the southerly wind in East Asia in the 850hPa layer was significantly stronger, which can be used as a climate monitoring and prediction index for the abnormally more summer precipitation in North China.
在线获得:April 06, 2023, DOI: 10.13878/j.cnki.dqkxxb.20221212001
Abstract:
During the Dragon-boat precipitation period in 2022 (from May 21 to June 21), the average precipitation in South China is 472.5 mm, which is the second largest in the same period in history, it is the third largest in Guangdong，Guangxi is the largest in the same period since there are meteorological records history in 1951. Under its influence, River basin flood occurs in the Pearl River basin, and catastrophic floods occurs in the Beijiang River basin. Using the daily precipitation data of 192 national meteorological observation stations in South China, NCEP/NCAR atmospheric circulation，atmospheric outward long wave radiation(OLR) data and NOAA monthly scale SST reanalysis data, the characteristics of South China Dragon-boat precipitation anomaly in 2022 and its relationship with atmospheric circulation and sea surface temperature are studied by using correlation and composite analysis methods. The results show that the East Asia Trough and the Northeast Cold Vortex are significantly strengthened, the cold air affecting South China is strong. The Western Pacific Subtropical High and the South Branch Trough are both relatively strong, and there is significant water vapor convergence in South China during the Dragon-boat precipitation period in 2022. The atmospheric response to the La Nina event is obvious in 2022, and the Walker circulation is enhanced. The convection in South China strengthened obviously. An abnormal anticyclone circulation was in the east of the Philippines. The Western Pacific Subtropical High becomes stronger, the southwest wind on its northwest side is stronger. Water vapor transmission to South China has increased significantly, Water vapor transmission to South China increased significantly. the significant enhancement of water vapor transport to South China. A significant upward movement at the same time in South China. The abnormality of atmospheric circulation and SST lead to the extreme Dragon-boat precipitation in South China in 2022.
During the Dragon-boat precipitation period in 2022 (from May 21 to June 21), the average precipitation in South China is 472.5 mm, which is the second largest in the same period in history, it is the third largest in Guangdong，Guangxi is the largest in the same period since there are meteorological records history in 1951. Under its influence, River basin flood occurs in the Pearl River basin, and catastrophic floods occurs in the Beijiang River basin. Using the daily precipitation data of 192 national meteorological observation stations in South China, NCEP/NCAR atmospheric circulation，atmospheric outward long wave radiation(OLR) data and NOAA monthly scale SST reanalysis data, the characteristics of South China Dragon-boat precipitation anomaly in 2022 and its relationship with atmospheric circulation and sea surface temperature are studied by using correlation and composite analysis methods. The results show that the East Asia Trough and the Northeast Cold Vortex are significantly strengthened, the cold air affecting South China is strong. The Western Pacific Subtropical High and the South Branch Trough are both relatively strong, and there is significant water vapor convergence in South China during the Dragon-boat precipitation period in 2022. The atmospheric response to the La Nina event is obvious in 2022, and the Walker circulation is enhanced. The convection in South China strengthened obviously. An abnormal anticyclone circulation was in the east of the Philippines. The Western Pacific Subtropical High becomes stronger, the southwest wind on its northwest side is stronger. Water vapor transmission to South China has increased significantly, Water vapor transmission to South China increased significantly. the significant enhancement of water vapor transport to South China. A significant upward movement at the same time in South China. The abnormality of atmospheric circulation and SST lead to the extreme Dragon-boat precipitation in South China in 2022.
在线获得:March 22, 2023, DOI: 10.13878/j.cnki.dqkxxb.20221025001
Abstract:
Based on the observational and reanalysis data during 1979—2020, this study investigated the basic features of the month-to-month reversal of air temperature anomalies in Northeast Asia in early summer (May to June) on the interannual timescales, as well as the effects of soil moisture anomalies in eastern Europe and the possible physical processes. The results show that the predominant mode of the intermonthly variation of air temperature anomalies in Northeast Asia in early summer is the reversal mode, that is warmer (colder) in May and colder (warmer) in June; and its formation is directly derived from the reversal of circulation anomalies over Northeast Asia. Further analysis indicates that the lower soil moisture in eastern Europe in May tends to lead to a warmer May and colder June in Northeast Asia. With the possible physical processes as following: lower soil moisture in May leads to local soil temperature and lower troposphere warming, which in turn causes a weakening (enhancement) of the lower tropospheric meridional temperature gradient and baroclinicity in the Mediterranean region (northern Europe). Correspondingly, high-frequency transient wave activity weakens (enhances) and facilitates the formation of an anomalous high over central and eastern Europe and hence Rossby wave source via transient vorticity forcing. The associated Rossby wave travels eastward along the polar front jet, resulting in a barotropic anomalous high over Northeast Asia and resultant surface warming. The soil moisture anomalies can persist until June but weaken in intensity. Similarly, it favors the formation of an anomalous high and Rossby wave source, but their center shifts westward to western Europe. The related Rossby wave activity leads to a barotropic anomalous low and hence cooling over Northeast Asia. There are obvious differences in the activity characteristics of Rossby waves (wave source, activity centers, and propagation pathway) associated with the soil moisture anomalies in eastern Europe in May and June, which are closely related to the difference in atmospheric climatology over northern Eurasia. When the soil moisture in eastern Europe is high in May, the above physical processes are roughly reversed.
Based on the observational and reanalysis data during 1979—2020, this study investigated the basic features of the month-to-month reversal of air temperature anomalies in Northeast Asia in early summer (May to June) on the interannual timescales, as well as the effects of soil moisture anomalies in eastern Europe and the possible physical processes. The results show that the predominant mode of the intermonthly variation of air temperature anomalies in Northeast Asia in early summer is the reversal mode, that is warmer (colder) in May and colder (warmer) in June; and its formation is directly derived from the reversal of circulation anomalies over Northeast Asia. Further analysis indicates that the lower soil moisture in eastern Europe in May tends to lead to a warmer May and colder June in Northeast Asia. With the possible physical processes as following: lower soil moisture in May leads to local soil temperature and lower troposphere warming, which in turn causes a weakening (enhancement) of the lower tropospheric meridional temperature gradient and baroclinicity in the Mediterranean region (northern Europe). Correspondingly, high-frequency transient wave activity weakens (enhances) and facilitates the formation of an anomalous high over central and eastern Europe and hence Rossby wave source via transient vorticity forcing. The associated Rossby wave travels eastward along the polar front jet, resulting in a barotropic anomalous high over Northeast Asia and resultant surface warming. The soil moisture anomalies can persist until June but weaken in intensity. Similarly, it favors the formation of an anomalous high and Rossby wave source, but their center shifts westward to western Europe. The related Rossby wave activity leads to a barotropic anomalous low and hence cooling over Northeast Asia. There are obvious differences in the activity characteristics of Rossby waves (wave source, activity centers, and propagation pathway) associated with the soil moisture anomalies in eastern Europe in May and June, which are closely related to the difference in atmospheric climatology over northern Eurasia. When the soil moisture in eastern Europe is high in May, the above physical processes are roughly reversed.
在线获得:February 17, 2023, DOI: 10.13878/j.cnki.dqkxxb.20220324001
Abstract:
Based on the daily observations collected by National Meteorological Information Centre since 1919, the homogenized daily maximum and minimum temperature series during the period of 1912-2019 were established for Baoding in Hebei Province. Firstly, the error data due to manual observation or recording, instrument malfunctions and digital inputs were removed by quality control. Then, the missing observations were interpolated by standardized series method using the homogenized daily temperature data in Tianjin over century-long scale. Lastly, several significant breakpoints caused by changes in interpolation, station relocation and instrument manufacturers were detected by penalized maximal t test (PMT) with annual and monthly reference series constructed by two means, and adjusted by Quantile Matching (QM) adjustment with daily reference series from Berkeley Earth-daily data. It was found that the characteristics of inter-annual, decadal and trend changes were consistent with those fromBerkeley Earth-monthly、CRUTS4.03 and GHCNV3. And the characteristics of warming change induced by the rapid urban development in Baoding region have well been reflected, simultaneously compared with the whole Beijing-Tianjin-Hebei region. In addition, the warming change of annual and seasonal temperature extremes in Baoding have also increased significantly since 1912, the warming trends of annual and autumn lowest minimum temperature (TNn) were 0.340℃/10a and 0.404℃/10a (95% significance level), respectively, and the corresponding diurnal temperature range (DTR) are -0.118℃/10a and -0.215℃/10a (95% significance level).
Based on the daily observations collected by National Meteorological Information Centre since 1919, the homogenized daily maximum and minimum temperature series during the period of 1912-2019 were established for Baoding in Hebei Province. Firstly, the error data due to manual observation or recording, instrument malfunctions and digital inputs were removed by quality control. Then, the missing observations were interpolated by standardized series method using the homogenized daily temperature data in Tianjin over century-long scale. Lastly, several significant breakpoints caused by changes in interpolation, station relocation and instrument manufacturers were detected by penalized maximal t test (PMT) with annual and monthly reference series constructed by two means, and adjusted by Quantile Matching (QM) adjustment with daily reference series from Berkeley Earth-daily data. It was found that the characteristics of inter-annual, decadal and trend changes were consistent with those fromBerkeley Earth-monthly、CRUTS4.03 and GHCNV3. And the characteristics of warming change induced by the rapid urban development in Baoding region have well been reflected, simultaneously compared with the whole Beijing-Tianjin-Hebei region. In addition, the warming change of annual and seasonal temperature extremes in Baoding have also increased significantly since 1912, the warming trends of annual and autumn lowest minimum temperature (TNn) were 0.340℃/10a and 0.404℃/10a (95% significance level), respectively, and the corresponding diurnal temperature range (DTR) are -0.118℃/10a and -0.215℃/10a (95% significance level).
在线获得:December 06, 2022, DOI: 10.13878/j.cnki.dqkxxb. 20210324001
Abstract:
A Rapid Refresh Hybrid system was constructed based on the Weather Research and Forecasting(WRF) Model,WRF Hybrid Data Assimilation system and Ensemble Transform Kalman Filter(ETKF),with assimilating both Wind Profile Radar Detection(WPRD) data and Microwave Radiometer(MWR) data.Experiments were performed for the study of the impact of 4 important parameters on the system,which are 2 tuning factors of static background error, localization scale and ensemble weighting factor,and contrast research was carried on to the results of Hybrid and 3DVAR schemes.Some encouraged conclusions were given as follows:Tuning these 4 parameters could improve performance of the Rapid Refresh Hybrid system.The analysis and forecast of Hybrid with pa-rameters not tuned were better than those of 3DVAR, and the results of Hybrid with parameters tuned were the best.
A Rapid Refresh Hybrid system was constructed based on the Weather Research and Forecasting(WRF) Model,WRF Hybrid Data Assimilation system and Ensemble Transform Kalman Filter(ETKF),with assimilating both Wind Profile Radar Detection(WPRD) data and Microwave Radiometer(MWR) data.Experiments were performed for the study of the impact of 4 important parameters on the system,which are 2 tuning factors of static background error, localization scale and ensemble weighting factor,and contrast research was carried on to the results of Hybrid and 3DVAR schemes.Some encouraged conclusions were given as follows:Tuning these 4 parameters could improve performance of the Rapid Refresh Hybrid system.The analysis and forecast of Hybrid with pa-rameters not tuned were better than those of 3DVAR, and the results of Hybrid with parameters tuned were the best.
在线获得:December 06, 2022, DOI: 10.13878/j.cnki.dqkxxb. 20220306001
Abstract:
Based on the Global Ensemble Prediction System in China Meteorological Administration (CMA-GEPS), the extreme Meiyu process over China in 2020 was evaluated. Results show that, during the Meiyu season, a strong and stable western Pacific subtropical high (WPSH) and a gradually strengthened East Asian summer monsoon provide favorable dynamic and moisture conditions for strong rainfall. For the western Pacific subtropical high, CMA-GEPS could skillfully forecast the evolution trend of WPSH index with 7-9 leading days; the CMA-GEPS prediction skills of WPSH strength and area are about the same level as the results from the NCEP ensemble prediction system, and the WPSH strength presents the weaker bias compared with the observation; the prediction skills of ridge line and western boundary index with CMA-GEPS are comparable with the results from ECMWF ensemble prediction system, and the forecasting bias is mainly attributed to the more southward location of ridge line and more eastward center position of western boundary. For the East Asian summer monsoon, CMA-GEPS could skillfully predict the index with 9 leading days, which was two days earlier than the control forecast. The CMA-GEPS control forecasting bias is mainly attributed to the weaker precipitation intensity and more southward location of strong rainfall belt, and also the control forecasting fails to predict the heavy rain in some regions in the middle and lower reaches of Yangtze river. With the time-spatial weight probability (TSWP) neighborhood scheme, the prediction skills related to the heavy rain with CMA-GEPS are improved obviously, and the scheme reduced the occurrence of precipitation missing report. The results of precipitation probability prediction were verified by observation and Brier Scores, which show that: the TSWP neighborhood scheme is superior to the original single point ensemble probability forecast method and the control forecast, and also has a well application values for the heavy storm prediction in the Meiyu period.
Based on the Global Ensemble Prediction System in China Meteorological Administration (CMA-GEPS), the extreme Meiyu process over China in 2020 was evaluated. Results show that, during the Meiyu season, a strong and stable western Pacific subtropical high (WPSH) and a gradually strengthened East Asian summer monsoon provide favorable dynamic and moisture conditions for strong rainfall. For the western Pacific subtropical high, CMA-GEPS could skillfully forecast the evolution trend of WPSH index with 7-9 leading days; the CMA-GEPS prediction skills of WPSH strength and area are about the same level as the results from the NCEP ensemble prediction system, and the WPSH strength presents the weaker bias compared with the observation; the prediction skills of ridge line and western boundary index with CMA-GEPS are comparable with the results from ECMWF ensemble prediction system, and the forecasting bias is mainly attributed to the more southward location of ridge line and more eastward center position of western boundary. For the East Asian summer monsoon, CMA-GEPS could skillfully predict the index with 9 leading days, which was two days earlier than the control forecast. The CMA-GEPS control forecasting bias is mainly attributed to the weaker precipitation intensity and more southward location of strong rainfall belt, and also the control forecasting fails to predict the heavy rain in some regions in the middle and lower reaches of Yangtze river. With the time-spatial weight probability (TSWP) neighborhood scheme, the prediction skills related to the heavy rain with CMA-GEPS are improved obviously, and the scheme reduced the occurrence of precipitation missing report. The results of precipitation probability prediction were verified by observation and Brier Scores, which show that: the TSWP neighborhood scheme is superior to the original single point ensemble probability forecast method and the control forecast, and also has a well application values for the heavy storm prediction in the Meiyu period.
在线获得:September 15, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20211121001
Abstract:
At present, a Regional Ensemble Prediction System has been developed by Kelamayi Meteorological Bureau. The system has only adopted initial condition perturbation of Ensemble Transform Kalman Filter(ETKF), and the system is lack of spread. In order to improve the skill of this Ensemble Prediction System, the model perturbation method of Stochastic Physics Parameterization Tendency(SPPT) is adopted and tested. This paper conducted sensitivity test on critical parameter of SPPT and parameter setting of SPPT is determined. Ensemble forecast experiment test is conducted and compared for both ETKF scheme and ETKF-SPPT scheme. The results show that the ETKF method can generate initial condition perturbation with dynamic structure, but the spread will saturated within short forecast lead time and will decrease due to the constraint of identical LBC for all members. With SPPT model perturbation method adopted, the ensemble spread can significantly improved. Ensemble verification scores indicate that the reliability of ETKF without model perturbation is small, and the root mean square error(RMSE) is relatively large, while add model perturbation to initial condition perturbation will improve the probabilistic forecast skill with larger forecast reliability and smaller(RMSE) . The results of gale forecast show that the adoption of model perturbation method can significantly improve the ensemble forecast that it has more accurate forecast on the magnitude and time period of local gale.
At present, a Regional Ensemble Prediction System has been developed by Kelamayi Meteorological Bureau. The system has only adopted initial condition perturbation of Ensemble Transform Kalman Filter(ETKF), and the system is lack of spread. In order to improve the skill of this Ensemble Prediction System, the model perturbation method of Stochastic Physics Parameterization Tendency(SPPT) is adopted and tested. This paper conducted sensitivity test on critical parameter of SPPT and parameter setting of SPPT is determined. Ensemble forecast experiment test is conducted and compared for both ETKF scheme and ETKF-SPPT scheme. The results show that the ETKF method can generate initial condition perturbation with dynamic structure, but the spread will saturated within short forecast lead time and will decrease due to the constraint of identical LBC for all members. With SPPT model perturbation method adopted, the ensemble spread can significantly improved. Ensemble verification scores indicate that the reliability of ETKF without model perturbation is small, and the root mean square error(RMSE) is relatively large, while add model perturbation to initial condition perturbation will improve the probabilistic forecast skill with larger forecast reliability and smaller(RMSE) . The results of gale forecast show that the adoption of model perturbation method can significantly improve the ensemble forecast that it has more accurate forecast on the magnitude and time period of local gale.
在线获得:September 07, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20211228001
Abstract:
As an important part of the cryosphere, snow cover has an important impact on atmospheric circulation and the hydrologic cycle. 98% of the seasonal snow in the world is located in the northern hemisphere. Eurasia continent is the main snow area in the Northern Hemisphere. China has a vast territory with nearly 50 degrees latitude across from north to south, and snow is widely distributed, which is important for studying spatial and temporal dynamics of snow cover. This paper proposes a trend extraction method based on empirical mode decomposition (EMD) based on the Snow Cover (SNC) data of FY-3 meteorological satellite and discusses the temporal and spatial characteristics and variation trends of Snow Cover in China during the past decade (2010-2019). The results show that: 1) China"s Snow Cover Frequency (SCF) has significant seasonal characteristics, which increases first and then decreases, and reaches the maximum SCF in February and March each year. The inter-annual SCF in northeast China has a significant trend of decline, but there is little change in other regions. 2) Snow Cover Rate (SCR) in Inner Mongolia and Xinjiang has decreased by 1.6% and 1.5% in the last decade, respectively. SCR in other areas has not changed significantly. SCR in main snow covered areas changed from increase to decrease in 2016.This study is of great significance to understand the spatial and temporal dynamics of typical snow cover areas in China and to further explore the relationship between snow cover and air temperature, precipitation, and other influential factors. At the same time, relevant results are obtained from the data of the Wind Cloud Meteorological Satellite, and the application of the observation data of the FY-3 satellite is strengthened.
As an important part of the cryosphere, snow cover has an important impact on atmospheric circulation and the hydrologic cycle. 98% of the seasonal snow in the world is located in the northern hemisphere. Eurasia continent is the main snow area in the Northern Hemisphere. China has a vast territory with nearly 50 degrees latitude across from north to south, and snow is widely distributed, which is important for studying spatial and temporal dynamics of snow cover. This paper proposes a trend extraction method based on empirical mode decomposition (EMD) based on the Snow Cover (SNC) data of FY-3 meteorological satellite and discusses the temporal and spatial characteristics and variation trends of Snow Cover in China during the past decade (2010-2019). The results show that: 1) China"s Snow Cover Frequency (SCF) has significant seasonal characteristics, which increases first and then decreases, and reaches the maximum SCF in February and March each year. The inter-annual SCF in northeast China has a significant trend of decline, but there is little change in other regions. 2) Snow Cover Rate (SCR) in Inner Mongolia and Xinjiang has decreased by 1.6% and 1.5% in the last decade, respectively. SCR in other areas has not changed significantly. SCR in main snow covered areas changed from increase to decrease in 2016.This study is of great significance to understand the spatial and temporal dynamics of typical snow cover areas in China and to further explore the relationship between snow cover and air temperature, precipitation, and other influential factors. At the same time, relevant results are obtained from the data of the Wind Cloud Meteorological Satellite, and the application of the observation data of the FY-3 satellite is strengthened.
在线获得:September 01, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20220623001
Abstract:
The observational stability, observational ability, and rationality of both base data and products of millimeter cloud radar (MCR for short) installed at Shanghai Meteorological Service during “The study and experiment of vertical integrated meteorological observation technology in mega-cities” (MCME for short) have been evaluated using observations from ground-based Lidar distrometer, micro-rain radar, radiosonde, FY-4A’s products as well as surface rain gage. The results show that: the observational stability of MCR is considerably high that only one-time software failure happened during the MCME and the data acquisition rate is larger than 95%. The minimal detectable reflectivity of MCR is generally distributed within -40 to -20 dBZ and has an exponential distribution in the vertical direction which fits well with the theory. The minimal detectable reflectivity of MCR changes little (smaller than 2 dB for heights lower than 9 km) during the MCME. A false “No Cloud” region will be seen in the MCR’s observation when the rain rate is larger than 4-5 mm/h. Although the raw data of the two MCRs have certain differences, the pattern of reflectivity in the time-height cross-section is highly reasonable indicated by the comparisons between the reflectivity from MCR’s observation, the reflectivity calculated with Lidar distrometer’s observation, and the reflectivity from micro-rain radar’s observation. The cloud top height and cloud base height retrieved by MCR are also evaluated by the cloud top/base height calculated by radiosonde and cloud top height retrieved by FY-4A satellite, and the results show certainly a degree of consistency among the three observations. The inconsistency of reflectivity due to the merge of multiple pulses with different pulse widths used in MCR’s base data processing and the false “clear sky area” due to the strong attenuation are two obvious issues that may have significant impacts on the operational usage of MCR. The advice on how to improve the current MCR from the perspective of the demand for the development of the Shanghai Meteorological Service has been given at the end of the paper.
The observational stability, observational ability, and rationality of both base data and products of millimeter cloud radar (MCR for short) installed at Shanghai Meteorological Service during “The study and experiment of vertical integrated meteorological observation technology in mega-cities” (MCME for short) have been evaluated using observations from ground-based Lidar distrometer, micro-rain radar, radiosonde, FY-4A’s products as well as surface rain gage. The results show that: the observational stability of MCR is considerably high that only one-time software failure happened during the MCME and the data acquisition rate is larger than 95%. The minimal detectable reflectivity of MCR is generally distributed within -40 to -20 dBZ and has an exponential distribution in the vertical direction which fits well with the theory. The minimal detectable reflectivity of MCR changes little (smaller than 2 dB for heights lower than 9 km) during the MCME. A false “No Cloud” region will be seen in the MCR’s observation when the rain rate is larger than 4-5 mm/h. Although the raw data of the two MCRs have certain differences, the pattern of reflectivity in the time-height cross-section is highly reasonable indicated by the comparisons between the reflectivity from MCR’s observation, the reflectivity calculated with Lidar distrometer’s observation, and the reflectivity from micro-rain radar’s observation. The cloud top height and cloud base height retrieved by MCR are also evaluated by the cloud top/base height calculated by radiosonde and cloud top height retrieved by FY-4A satellite, and the results show certainly a degree of consistency among the three observations. The inconsistency of reflectivity due to the merge of multiple pulses with different pulse widths used in MCR’s base data processing and the false “clear sky area” due to the strong attenuation are two obvious issues that may have significant impacts on the operational usage of MCR. The advice on how to improve the current MCR from the perspective of the demand for the development of the Shanghai Meteorological Service has been given at the end of the paper.
在线获得:July 07, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20220503001
Abstract:
Based on the satellite derived snow depth over the Tibetan Plateau (TP), daily rainfall data of 261 meteorological stations in South China (SC) and the ERA5 reanalysis dataset during 1979-2018, relationships between the TP winter snow depth and precipitation during the first rainy season (FRS) in SC have been investigated. Results show that: (1) Connections between snow depth over the western TP and precipitation during the FRS in SC are the most robust and the TP snow depth mainly affects frontal precipitation of the FSR, whereas it shows less impact on summer monsoonal rainfall. (2) Onset date of the FRS in above normal TP snow years is about 20 days earlier than that in below normal snow years, leading to more rainy days, longer of the FRS and more rainfall during the FRS. However, rainfall intensity during the FRS shows small difference between different types of the TP snow years. (3) The TP is colder in above normal snow year and the cooling effect stimulates an abnormal anticyclonic circulation over the TP. However, tripole anomaly patterns of 500 hPa geopotential height occur at East Asian coastal region. Configurations of the circulation facilitate cold air in middle-high latitude regions invade SC, which makes a colder SC. And the enhanced northwest Pacific subtropical high intensifies the low-level southerly flow and water vapor supplement. The front swings northward-southward in northern part of the SC during March-April. The FRS has been established once the dry cold northerly and the warm-wet southerly flow invade the SC in early April. In below normal TP snow years, both the cold northerly and warm southerly flow are weak and inactivity and the front in northern part of the SC is interrupted in early April. When the northerly and southerly flow invades the SC in mid-to-late April, the front is reverted and the FRS occurs later.
Based on the satellite derived snow depth over the Tibetan Plateau (TP), daily rainfall data of 261 meteorological stations in South China (SC) and the ERA5 reanalysis dataset during 1979-2018, relationships between the TP winter snow depth and precipitation during the first rainy season (FRS) in SC have been investigated. Results show that: (1) Connections between snow depth over the western TP and precipitation during the FRS in SC are the most robust and the TP snow depth mainly affects frontal precipitation of the FSR, whereas it shows less impact on summer monsoonal rainfall. (2) Onset date of the FRS in above normal TP snow years is about 20 days earlier than that in below normal snow years, leading to more rainy days, longer of the FRS and more rainfall during the FRS. However, rainfall intensity during the FRS shows small difference between different types of the TP snow years. (3) The TP is colder in above normal snow year and the cooling effect stimulates an abnormal anticyclonic circulation over the TP. However, tripole anomaly patterns of 500 hPa geopotential height occur at East Asian coastal region. Configurations of the circulation facilitate cold air in middle-high latitude regions invade SC, which makes a colder SC. And the enhanced northwest Pacific subtropical high intensifies the low-level southerly flow and water vapor supplement. The front swings northward-southward in northern part of the SC during March-April. The FRS has been established once the dry cold northerly and the warm-wet southerly flow invade the SC in early April. In below normal TP snow years, both the cold northerly and warm southerly flow are weak and inactivity and the front in northern part of the SC is interrupted in early April. When the northerly and southerly flow invades the SC in mid-to-late April, the front is reverted and the FRS occurs later.
在线获得:June 22, 2022, DOI: 10.13878/j.cnki.dqkxxb.20220406001
Abstract:
The key dynamic forcing factors and sources of water vapor influencing the remote heavy rainfall over North China produced by Typhoon Lekima on 10 August 2019 are investigated using the surface rain gauge data, ERA5 reanalysis data, and ECWMF ensemble forecasts data. Ensemble sensitivity analysis shows that low-level relative vorticity over the remote typhoon precipitation (TRP) area is the most significant dynamic factor facilitating TRP. The stronger low-level relative vorticity is closely associated with the enhancement of northerly winds in the north caused by the deepening of the low-level short-wave trough and jointly related to the strengthening low-level southerly winds between the TRP and typhoon precipitation (TP) area. The divergence term of relative vorticity is the crucial dynamic process that dominates the strengthening and weakening period of TRP. During the TRP enhancement phase, the positive vorticity is mainly contributed by the positive divergence term, while the negative divergence term leads to the decrease of relative vorticity and the weakening of TRP intensity. The water vapor in the TRP region at 500 hPa is mainly contributed by local area and Typhoon Lekima, while 60% of the water vapor at 700 hPa is contributed by Typhoon Lekima. 40% of the water vapor at 850 hPa is contributed by Typhoon Rosa, and the remaining 60% of water vapor is transported from Typhoon Lekima and the local area.
The key dynamic forcing factors and sources of water vapor influencing the remote heavy rainfall over North China produced by Typhoon Lekima on 10 August 2019 are investigated using the surface rain gauge data, ERA5 reanalysis data, and ECWMF ensemble forecasts data. Ensemble sensitivity analysis shows that low-level relative vorticity over the remote typhoon precipitation (TRP) area is the most significant dynamic factor facilitating TRP. The stronger low-level relative vorticity is closely associated with the enhancement of northerly winds in the north caused by the deepening of the low-level short-wave trough and jointly related to the strengthening low-level southerly winds between the TRP and typhoon precipitation (TP) area. The divergence term of relative vorticity is the crucial dynamic process that dominates the strengthening and weakening period of TRP. During the TRP enhancement phase, the positive vorticity is mainly contributed by the positive divergence term, while the negative divergence term leads to the decrease of relative vorticity and the weakening of TRP intensity. The water vapor in the TRP region at 500 hPa is mainly contributed by local area and Typhoon Lekima, while 60% of the water vapor at 700 hPa is contributed by Typhoon Lekima. 40% of the water vapor at 850 hPa is contributed by Typhoon Rosa, and the remaining 60% of water vapor is transported from Typhoon Lekima and the local area.
在线获得:June 06, 2022, DOI: 10.13878/j.cnki.dqkxxb.20220215001
Abstract:
In recent years, O3 pollution in Nanjing has gradually intensified. In order to understand the impact of surrounding cities on O3 in Nanjing, we simulated the air quality in the Yangtze River Delta(YRD) using the source-oriented WRF/CMAQ regional air quality model from March to October 2018.By tracking the emissions from 41 cities in the YRD and outside the YRD, the characteristics of the transportation of O3 in Nanjing were analyzed under different winds. The results show that from March to October 2018, Nanjing contributes 56.8% of local O3 on polluted days, and contributed 49.5% on polluted days. When pollution occurs, the contribution of surrounding cities to Nanjing increased from 36.4% to 46.3%, and the contribution from outside the YRD to Nanjing decreased from 6.8% to 4.7%. From the perspective of daily changes, Nanjing O3 accounts for more than 70% of the local contribution during the daytime, and at night, most of the transport contributions come from surrounding cities and areas outside the YRD (more than 95%) .On all O3 pollution days, the contribution in Jiangsu Province accounts for 73.1%, 10.8% in Zhejiang Province, 10.5% in Anhui Province and 0.95% in Shanghai. During the pollution period, the contribution ratio of O3 (O3N) produced by NOx as a precursor in Nanjing to O3 (O3V) produced by VOCs as a precursor was about 7:13; During all polluting periods, the proportion of southeast and northeast winds was 38.0% and 35.9% respectively, when the dominant wind direction is northeast, Nanjing has the highest concentration of non-background O3, and the contribution of upwind to Yangzhou reached 13.6%. When the dominant wind direction is southeast wind, the upwind to Changzhou (7.3%), Zhenjiang (7.0%) and Wuxi (6.5%) contributed more to Nanjing O3. Therefore, Nanjing O3 pollution control should consider the transportation of upwind to cities.
In recent years, O3 pollution in Nanjing has gradually intensified. In order to understand the impact of surrounding cities on O3 in Nanjing, we simulated the air quality in the Yangtze River Delta(YRD) using the source-oriented WRF/CMAQ regional air quality model from March to October 2018.By tracking the emissions from 41 cities in the YRD and outside the YRD, the characteristics of the transportation of O3 in Nanjing were analyzed under different winds. The results show that from March to October 2018, Nanjing contributes 56.8% of local O3 on polluted days, and contributed 49.5% on polluted days. When pollution occurs, the contribution of surrounding cities to Nanjing increased from 36.4% to 46.3%, and the contribution from outside the YRD to Nanjing decreased from 6.8% to 4.7%. From the perspective of daily changes, Nanjing O3 accounts for more than 70% of the local contribution during the daytime, and at night, most of the transport contributions come from surrounding cities and areas outside the YRD (more than 95%) .On all O3 pollution days, the contribution in Jiangsu Province accounts for 73.1%, 10.8% in Zhejiang Province, 10.5% in Anhui Province and 0.95% in Shanghai. During the pollution period, the contribution ratio of O3 (O3N) produced by NOx as a precursor in Nanjing to O3 (O3V) produced by VOCs as a precursor was about 7:13; During all polluting periods, the proportion of southeast and northeast winds was 38.0% and 35.9% respectively, when the dominant wind direction is northeast, Nanjing has the highest concentration of non-background O3, and the contribution of upwind to Yangzhou reached 13.6%. When the dominant wind direction is southeast wind, the upwind to Changzhou (7.3%), Zhenjiang (7.0%) and Wuxi (6.5%) contributed more to Nanjing O3. Therefore, Nanjing O3 pollution control should consider the transportation of upwind to cities.
在线获得:June 02, 2022, DOI: 10.13878/j.cnki.dqkxxb.20210811002
Abstract:
Based on the daily precipitation of 24 global climate models from the sixth phase of the Coupled Model Intercomparison Project 6 (CMIP6) multimodel simulations, the generalized extreme value distribution (GEV) is introduced to study the risks of precipitation extremes that expected to occur every 20, 50 and 100 years over China under 1.5/2°C global warming. Compared to the historical period (1995-2014), the changes in the probability risks of extreme precipitation under global warming of 1.5/2℃ present an overall increasing trend. Although their spatial distributions show similar characteristics, the additional half a degree global warming will lead to a higher risk. For example, extreme precipitation that occurred once every 50 years will become once about every 17/14years under the warming of 1.5/2°C, respectively, and extreme precipitation will become more frequent. There are regional differences in the responses of different regions to climate warming, among which the middle and upper reaches of the Yangtze and Yellow Rivers and the Qinghai-Tibet Plateau region in the Western China, and the middle and lower reaches of Yangtze and Yellow Rivers and its south in Eastern China, are highly sensitive response regions to climate change, and their probability ratios reach 3 or even 5 or more. Furthermore, the influence and contribution measures of location and scale parameters on the probability ratios are explored from a theoretical perspective based on the probability distributions, and are used to explore the influences of the climate means and variability changes on the risks of extreme precipitation. The results show that there are significant differences between Eastern China and Western China in the incremental changes of location and scale parameters, and in the rates of probability risk changes, which lead to differences of the factors that influencing the risk of extreme precipitation. In the Western China, although the changes in climate means and variabilities of extreme precipitation are small, the probability ratios increase significantly due to the high rates of probability risk changes. In contrast, the change rates are small, but the climate means and variabilities present a high increase, which also lead to an increase in the Eastern China. Moreover, compared to the location parameters, the increase risks in most of regions over China are mainly due to the changes of scale parameters of extreme precipitation in the future.
Based on the daily precipitation of 24 global climate models from the sixth phase of the Coupled Model Intercomparison Project 6 (CMIP6) multimodel simulations, the generalized extreme value distribution (GEV) is introduced to study the risks of precipitation extremes that expected to occur every 20, 50 and 100 years over China under 1.5/2°C global warming. Compared to the historical period (1995-2014), the changes in the probability risks of extreme precipitation under global warming of 1.5/2℃ present an overall increasing trend. Although their spatial distributions show similar characteristics, the additional half a degree global warming will lead to a higher risk. For example, extreme precipitation that occurred once every 50 years will become once about every 17/14years under the warming of 1.5/2°C, respectively, and extreme precipitation will become more frequent. There are regional differences in the responses of different regions to climate warming, among which the middle and upper reaches of the Yangtze and Yellow Rivers and the Qinghai-Tibet Plateau region in the Western China, and the middle and lower reaches of Yangtze and Yellow Rivers and its south in Eastern China, are highly sensitive response regions to climate change, and their probability ratios reach 3 or even 5 or more. Furthermore, the influence and contribution measures of location and scale parameters on the probability ratios are explored from a theoretical perspective based on the probability distributions, and are used to explore the influences of the climate means and variability changes on the risks of extreme precipitation. The results show that there are significant differences between Eastern China and Western China in the incremental changes of location and scale parameters, and in the rates of probability risk changes, which lead to differences of the factors that influencing the risk of extreme precipitation. In the Western China, although the changes in climate means and variabilities of extreme precipitation are small, the probability ratios increase significantly due to the high rates of probability risk changes. In contrast, the change rates are small, but the climate means and variabilities present a high increase, which also lead to an increase in the Eastern China. Moreover, compared to the location parameters, the increase risks in most of regions over China are mainly due to the changes of scale parameters of extreme precipitation in the future.
在线获得:May 23, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20220503002
Abstract:
A wind profiler radar is a new type of Doppler wind radar designed to measure the horizontal wind vectors, and are able to provide wind profiles at various elevations with high temporal and spatial resolution. It plays an increasingly important role in China"s Meteorological business. However, the quality of wind profiler radar data varies widely consider-ing the different types of radars deployed in China. To better utilize these data in numerical weather prediction (NWP), this study assesses the data quality from five different types of wind profiler radars (i.e., CFL-06, GLC-24, TWP8-L, CFL-03 and CLC-11-D) at 20 stations in North China from June to September 2019. Results show that each type of profiler has strong detection ability, but different profilers have great differences in data acquisition rate and effective detection height. Without considering the influence of precipitation, the data quality of v-wind is better than that of u-wind for all types of profilers. In all air conditions, the quality of u-wind data from TWP8-L profiler is relatively the best, followed by CFL-03 radar, the quality of u-wind data from GLC-24 radar is the worst. The differences in v-wind data is not noticea-ble, while bias correction and quality control are necessary for u-wind data. The data quality of wind profile radar is quite sensitive to precipitation, which reduces the data acquisition rates at low levels and increases the data acquisition rates at the middle and high levels, with the maximum increase of 53%. The statistical results indicate precipitation is responsible for the large mean errors and root mean square errors (RMSEs) of u wind data. GLC-24 and CLC-11-D profilers are the most sensitive to precipitation. The RMSEs are increased by 5.5 m/s in precipitation regions as compared to non-precipitation regions. The u-wind and v-wind data in precipitation regions requires further quality control procedure.
A wind profiler radar is a new type of Doppler wind radar designed to measure the horizontal wind vectors, and are able to provide wind profiles at various elevations with high temporal and spatial resolution. It plays an increasingly important role in China"s Meteorological business. However, the quality of wind profiler radar data varies widely consider-ing the different types of radars deployed in China. To better utilize these data in numerical weather prediction (NWP), this study assesses the data quality from five different types of wind profiler radars (i.e., CFL-06, GLC-24, TWP8-L, CFL-03 and CLC-11-D) at 20 stations in North China from June to September 2019. Results show that each type of profiler has strong detection ability, but different profilers have great differences in data acquisition rate and effective detection height. Without considering the influence of precipitation, the data quality of v-wind is better than that of u-wind for all types of profilers. In all air conditions, the quality of u-wind data from TWP8-L profiler is relatively the best, followed by CFL-03 radar, the quality of u-wind data from GLC-24 radar is the worst. The differences in v-wind data is not noticea-ble, while bias correction and quality control are necessary for u-wind data. The data quality of wind profile radar is quite sensitive to precipitation, which reduces the data acquisition rates at low levels and increases the data acquisition rates at the middle and high levels, with the maximum increase of 53%. The statistical results indicate precipitation is responsible for the large mean errors and root mean square errors (RMSEs) of u wind data. GLC-24 and CLC-11-D profilers are the most sensitive to precipitation. The RMSEs are increased by 5.5 m/s in precipitation regions as compared to non-precipitation regions. The u-wind and v-wind data in precipitation regions requires further quality control procedure.
在线获得:May 06, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20220207001
Abstract:
We proposed a novel method to predict the meteorological risk for important events, and successfully applied it to the 70th anniversary celebration of the People"s Republic of China. The method based on fine meteorological factors analysis, considered risk resources(weather risk) and vulnerability(impact on events), so that efficient measures can be taken in advance to make sure the success of the events. A risk matrix method was used to perform the analysis and prediction. Meteorological risk levels were evaluated and risk maps were drawn of major celebration events based on the investigation and analysis of meteorological factors that may affected the successful holding of the celebration events and the occurrence ratio of different meteorological events. For the 70th anniversary celebration of the People"s Republic of China, we recognized the major weather risks (from high to low) were precipitation/overcast and rain, day force eight wind, frog-haze, night force eight wind, high temperature and direct sunlight, thunder and lightning, low temperature, etc.. Risk control measures and countermeasures were put forward to the risk caused by different meteorological factors, which provided important reference for the scientific formulation and implementation of safety work plan and the successful development of activities. In the meteorological support work of major activities, a major change has been realized from considering only the occurrence probability of high impact weather to risk assessment based on weather impact and we got a good service effect for the events.
We proposed a novel method to predict the meteorological risk for important events, and successfully applied it to the 70th anniversary celebration of the People"s Republic of China. The method based on fine meteorological factors analysis, considered risk resources(weather risk) and vulnerability(impact on events), so that efficient measures can be taken in advance to make sure the success of the events. A risk matrix method was used to perform the analysis and prediction. Meteorological risk levels were evaluated and risk maps were drawn of major celebration events based on the investigation and analysis of meteorological factors that may affected the successful holding of the celebration events and the occurrence ratio of different meteorological events. For the 70th anniversary celebration of the People"s Republic of China, we recognized the major weather risks (from high to low) were precipitation/overcast and rain, day force eight wind, frog-haze, night force eight wind, high temperature and direct sunlight, thunder and lightning, low temperature, etc.. Risk control measures and countermeasures were put forward to the risk caused by different meteorological factors, which provided important reference for the scientific formulation and implementation of safety work plan and the successful development of activities. In the meteorological support work of major activities, a major change has been realized from considering only the occurrence probability of high impact weather to risk assessment based on weather impact and we got a good service effect for the events.
在线获得:May 06, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20211230001
Abstract:
Nowadays, ensemble forecasting has become the main support for operational weather forecasting. However, due to the limitations and imperfections of the numerical model itself, the forecast results are generally systematically biased. In addition, different forecasting models usually have different physical parameterization schemes, initial conditions, etc., resulting in different forecasting capabilities. Therefore, how to eliminate systematic biases and how to make full and effective use of forecast information from different models to obtain more accurate weather forecasts has received extensive attention. In recent years, using statistical theory and forecasting diagnosis, multimodel ensemble forecasting technologies based on multiple ensemble prediction systems have been rapidly developed to effectively eliminate systematic biases and improve weather forecasting skills. For the three most basic surface meteorological variables (i.e., temperature, precipitation, wind), the widely used multimodel ensemble technologies such as ensemble mean (EM), bias-removed ensemble mean (BREM), superensemble (SUP), Bayesian model averaging(BMA), and ensemble model output statistics(EMOS) are first introduced from the perspective of deterministic forecasting and probabilistic forecasting. Finally, the issues that need to be paid attention to when using and developing multimodel ensemble technologies are discussed, including the consideration the number of participating models, the development of categorized precipitation and wind speed forecast models. Meanwhile, the combination of multimodel ensemble with machine learning deserves more investigation.
Nowadays, ensemble forecasting has become the main support for operational weather forecasting. However, due to the limitations and imperfections of the numerical model itself, the forecast results are generally systematically biased. In addition, different forecasting models usually have different physical parameterization schemes, initial conditions, etc., resulting in different forecasting capabilities. Therefore, how to eliminate systematic biases and how to make full and effective use of forecast information from different models to obtain more accurate weather forecasts has received extensive attention. In recent years, using statistical theory and forecasting diagnosis, multimodel ensemble forecasting technologies based on multiple ensemble prediction systems have been rapidly developed to effectively eliminate systematic biases and improve weather forecasting skills. For the three most basic surface meteorological variables (i.e., temperature, precipitation, wind), the widely used multimodel ensemble technologies such as ensemble mean (EM), bias-removed ensemble mean (BREM), superensemble (SUP), Bayesian model averaging(BMA), and ensemble model output statistics(EMOS) are first introduced from the perspective of deterministic forecasting and probabilistic forecasting. Finally, the issues that need to be paid attention to when using and developing multimodel ensemble technologies are discussed, including the consideration the number of participating models, the development of categorized precipitation and wind speed forecast models. Meanwhile, the combination of multimodel ensemble with machine learning deserves more investigation.
在线获得:March 23, 2022, DOI: 10.13878/j.cnki.dqkxxb. 20211229001
Abstract:
To study the differences and similarities between fine observation and conventional observation in fog microstructure, a systematic field observation of fog was conducted and lasted 58 days in Donghai, Lianyungang during the winter of 2020. Two fog monitors with different frequencies (5Hz and 1Hz) were applied simultaneously for measuring a radiation fog on December 28, 2020. Based on this observation, we found that compared with the 1Hz data, the 5Hz measurement observed more extreme values. In terms of the whole fog process, the results of the 5Hz fog droplet spectrometer averaged into 1Hz are less similar to the original results of 1Hz in the fog generation and dissipation stages, and more similar in the fog maturation and development stages. In terms of the spectral patterns, the spectral patterns of 5 Hz and 1 Hz are similar, while the main differences appear at the peak. The measurements of 5 Hz and 1 Hz can both reflect the relationship between the microphysics at different stages of this fog process, and the main differences appear at the fog generation stage. This may be due to the relatively small number of activation and condensation growth of new fog droplets observed at 5Hz at this stage.
To study the differences and similarities between fine observation and conventional observation in fog microstructure, a systematic field observation of fog was conducted and lasted 58 days in Donghai, Lianyungang during the winter of 2020. Two fog monitors with different frequencies (5Hz and 1Hz) were applied simultaneously for measuring a radiation fog on December 28, 2020. Based on this observation, we found that compared with the 1Hz data, the 5Hz measurement observed more extreme values. In terms of the whole fog process, the results of the 5Hz fog droplet spectrometer averaged into 1Hz are less similar to the original results of 1Hz in the fog generation and dissipation stages, and more similar in the fog maturation and development stages. In terms of the spectral patterns, the spectral patterns of 5 Hz and 1 Hz are similar, while the main differences appear at the peak. The measurements of 5 Hz and 1 Hz can both reflect the relationship between the microphysics at different stages of this fog process, and the main differences appear at the fog generation stage. This may be due to the relatively small number of activation and condensation growth of new fog droplets observed at 5Hz at this stage.
Accounting the carbon sequestration rate of terrestrial ecosystems: methods, progress and challenges
在线获得:March 14, 2022, DOI: 10.13878/j.cnki.dqkxxb. 20220303002
Abstract:
Global warming, caused by the rapid increase of atmospheric CO2 has become an urgent problem for sustainable development of human beings. Terrestrial ecosystems have played an important carbon sink role in the past decades by absorbing about 30% of the CO2 emitted by human activities. This paper reviewed the estimation methods of carbon sequestration rate of terrestrial ecosystems, including sampling inventory, flux monitoring, model simulation, remote sensing, etc., and analyzed the progresses and challenges of the current approaches for accounting terrestrial carbon sequestration. Sampling inventory and flux observations can provide direct measurement of plot-scale carbon sequestration rate, but there are many problems, such as limited observation samples and insufficient spatial representation. Model simulation methods can describe the terrestrial carbon cycles and simulate the state and change of carbon sequestration rates in terrestrial ecosystems. However, the approximating and simplifying processes in model, together with the uncertainties introduced by model-driven data, it is very challenging to accurately modeling the carbon sequestration rate of terrestrial ecosystems. Satellite remote sensing, holding the advantages of global coverage, fine resolution, and time-series observations, combined with machine learning methods, can provide a new approach for the estimation of the carbon sequestration rate of terrestrial ecosystems. So far, various accounting methods for carbon sequestration rates have not yet met the needs of monitoring carbon sequestration in terrestrial ecosystems due to the high spatial and temporal heterogeneity. In the future, it is extremely important to integrate various accounting approaches, such as ground observations, model simulations, and satellite remote sensing, to provide accurate estimation of terrestrial ecosystem carbon sinks at the regional and global scales.
Global warming, caused by the rapid increase of atmospheric CO2 has become an urgent problem for sustainable development of human beings. Terrestrial ecosystems have played an important carbon sink role in the past decades by absorbing about 30% of the CO2 emitted by human activities. This paper reviewed the estimation methods of carbon sequestration rate of terrestrial ecosystems, including sampling inventory, flux monitoring, model simulation, remote sensing, etc., and analyzed the progresses and challenges of the current approaches for accounting terrestrial carbon sequestration. Sampling inventory and flux observations can provide direct measurement of plot-scale carbon sequestration rate, but there are many problems, such as limited observation samples and insufficient spatial representation. Model simulation methods can describe the terrestrial carbon cycles and simulate the state and change of carbon sequestration rates in terrestrial ecosystems. However, the approximating and simplifying processes in model, together with the uncertainties introduced by model-driven data, it is very challenging to accurately modeling the carbon sequestration rate of terrestrial ecosystems. Satellite remote sensing, holding the advantages of global coverage, fine resolution, and time-series observations, combined with machine learning methods, can provide a new approach for the estimation of the carbon sequestration rate of terrestrial ecosystems. So far, various accounting methods for carbon sequestration rates have not yet met the needs of monitoring carbon sequestration in terrestrial ecosystems due to the high spatial and temporal heterogeneity. In the future, it is extremely important to integrate various accounting approaches, such as ground observations, model simulations, and satellite remote sensing, to provide accurate estimation of terrestrial ecosystem carbon sinks at the regional and global scales.
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2019,42(2): 161-173, DOI: 10.13878/j.cnki.dqkxxb.20170504012
Abstract:
The eyewall replacement cycle plays an important role in changes regarding typhoon intensity and inner-core structure.In this study,in order to investigate the influences of large-eddy simulation(LES) on eyewall replacement,two ideal numerical experiments were conducted,of which one was coupled with LES.The study results indicate that the typhoon intensity of the LES experiment was stronger with larger inflow in the boundary layers.It took the two typhoon approximately 20-22 hours to complete the entire eyewall replacement,but the typhoon in the LES experiment had a faster enclosing of the outer eyewall.At the same time,the intensity and updraft in the outer eyewall were also greater.After the eyewall replacement,the typhoon in the LES experiment continued to intensify,and its intensity became greater than it was before the eyewall replacement.Of more importance is that the LES can more effectively simulate the downdraft within the moat region which is at the outside of the inner eyewall.In addition,the downward motion can more effectively induce the formation and development of convections near the outer eyewall regions,and is in line with the observational features found by previous studies.
The eyewall replacement cycle plays an important role in changes regarding typhoon intensity and inner-core structure.In this study,in order to investigate the influences of large-eddy simulation(LES) on eyewall replacement,two ideal numerical experiments were conducted,of which one was coupled with LES.The study results indicate that the typhoon intensity of the LES experiment was stronger with larger inflow in the boundary layers.It took the two typhoon approximately 20-22 hours to complete the entire eyewall replacement,but the typhoon in the LES experiment had a faster enclosing of the outer eyewall.At the same time,the intensity and updraft in the outer eyewall were also greater.After the eyewall replacement,the typhoon in the LES experiment continued to intensify,and its intensity became greater than it was before the eyewall replacement.Of more importance is that the LES can more effectively simulate the downdraft within the moat region which is at the outside of the inner eyewall.In addition,the downward motion can more effectively induce the formation and development of convections near the outer eyewall regions,and is in line with the observational features found by previous studies.
2022,45(2): 280-291, DOI: 10.13878/j.cnki.dqkxxb.20200719017
Abstract:
Based on the sea surface temperature (SST) data from NOAA in USA, the asymmetric characteristics of interannual relationship between ENSO and Victoria mode (VM;EOF2 of North Pacific SST anomalies in winter (DJF)) were emphatically analyzed.Results show that the correlation between VM and ENSO is weak on the decadal scale, but strong on the interannual scale.VM has significant negative correlation with ENSO in the same year, and has strong positive correlation with ENSO in the following year.However, there is a certain asymmetry in the relationship between the positive/negative VM events and ENSO warm/cold phases on the interannual scale.The relationship between the positive VM events and the SST anomalies in the tropical central and eastern Pacific in the same winter is weak, but El Niño events often occur in the following year.In contrast, the negative VM events are usually accompanied by El Niño events in the same years, but there is no significant relationship between the negative VM events and the SST anomalies in the tropical central and eastern Pacific in the following winter and there are few ENSO events.It can be seen that the positive VM event seems to promote the occurrence and development of El Niño in the next year and can be used as one of the early prediction factors of ENSO, while the negative VM event cannot be used as the early prediction factor of ENSO.
Based on the sea surface temperature (SST) data from NOAA in USA, the asymmetric characteristics of interannual relationship between ENSO and Victoria mode (VM;EOF2 of North Pacific SST anomalies in winter (DJF)) were emphatically analyzed.Results show that the correlation between VM and ENSO is weak on the decadal scale, but strong on the interannual scale.VM has significant negative correlation with ENSO in the same year, and has strong positive correlation with ENSO in the following year.However, there is a certain asymmetry in the relationship between the positive/negative VM events and ENSO warm/cold phases on the interannual scale.The relationship between the positive VM events and the SST anomalies in the tropical central and eastern Pacific in the same winter is weak, but El Niño events often occur in the following year.In contrast, the negative VM events are usually accompanied by El Niño events in the same years, but there is no significant relationship between the negative VM events and the SST anomalies in the tropical central and eastern Pacific in the following winter and there are few ENSO events.It can be seen that the positive VM event seems to promote the occurrence and development of El Niño in the next year and can be used as one of the early prediction factors of ENSO, while the negative VM event cannot be used as the early prediction factor of ENSO.
Abstract:
Based on the hourly precipitation observed by automatic weather stations(AWS) in China and retrieved from CMORPH(CPC MORPHing technique) satellite data,the merged precipitation product at hourly/0.1°lat/0.1°lon temporal-spatial resolution in China is developed through the two-step merging algorithm of PDF(probability density function) and OI(optimal interpolation).In this paper,the quality of merged precipitation product is assessed from the points of temporal-spatial characteristics of error,accuracy at different precipitation rates and cumulative times,merging effect at three station network densities and monitoring capability of the heavy rainfall.Results indicate that:1)The merged precipitation product effectively uses the advantages of AWS observations and satellite product of CMORPH,so it is more reasonable both at the precipitation amount and spatial distribution;2)The regional mean bias and root-mean-square error of the merged precipitation product are decreased remarkably,and they have a little change with time;3)The relative bias of merged precipitation product is -1.675%,less than 15% and about 30% for the medium(1.0—2.5 mm/h),medium to large(1.0—8.0 mm/h) and heavy rainfall(≥8.0 mm/h),respectively,and the product quality is improved further with the cumulative time increases.The merged precipitation product can capture the precipitation process very well and have a definite advantage in the quantitatively rainfall monitoring.
Based on the hourly precipitation observed by automatic weather stations(AWS) in China and retrieved from CMORPH(CPC MORPHing technique) satellite data,the merged precipitation product at hourly/0.1°lat/0.1°lon temporal-spatial resolution in China is developed through the two-step merging algorithm of PDF(probability density function) and OI(optimal interpolation).In this paper,the quality of merged precipitation product is assessed from the points of temporal-spatial characteristics of error,accuracy at different precipitation rates and cumulative times,merging effect at three station network densities and monitoring capability of the heavy rainfall.Results indicate that:1)The merged precipitation product effectively uses the advantages of AWS observations and satellite product of CMORPH,so it is more reasonable both at the precipitation amount and spatial distribution;2)The regional mean bias and root-mean-square error of the merged precipitation product are decreased remarkably,and they have a little change with time;3)The relative bias of merged precipitation product is -1.675%,less than 15% and about 30% for the medium(1.0—2.5 mm/h),medium to large(1.0—8.0 mm/h) and heavy rainfall(≥8.0 mm/h),respectively,and the product quality is improved further with the cumulative time increases.The merged precipitation product can capture the precipitation process very well and have a definite advantage in the quantitatively rainfall monitoring.
2014,37(5): 642-652, DOI: 10.13878/j.cnki.dqkxxb.20121017006
Abstract:
In this paper,the Weather Research and Forecast Model(WRF) is coupled with Surface-Layer Scheme,Single-Layer Urban Canopy Model and Mingle-Layer Urban Canopy Model respectively to evaluate the simulation effect of various parameterizations on the weather conditions on 1 August 2007 in Nanjing.The best urban parameterization scheme is coupled into WRF to study the impact of land cover change on the Urban Heat Island(UHI) effect in Nanjing.Results show that the Mingle-Layer Urban Canopy Model shows the best simulation effect for surface temperature and 10m wind field.Urbanization makes surface air temperature increase over the region,especially at night and thus intensifies the UHI effect.After urbanization,the wind speed in the downtown area decreases obviously while the Urban Heat Circulation occurs more apparently.There also exists the downstream effect of UHI in Nanjing.
In this paper,the Weather Research and Forecast Model(WRF) is coupled with Surface-Layer Scheme,Single-Layer Urban Canopy Model and Mingle-Layer Urban Canopy Model respectively to evaluate the simulation effect of various parameterizations on the weather conditions on 1 August 2007 in Nanjing.The best urban parameterization scheme is coupled into WRF to study the impact of land cover change on the Urban Heat Island(UHI) effect in Nanjing.Results show that the Mingle-Layer Urban Canopy Model shows the best simulation effect for surface temperature and 10m wind field.Urbanization makes surface air temperature increase over the region,especially at night and thus intensifies the UHI effect.After urbanization,the wind speed in the downtown area decreases obviously while the Urban Heat Circulation occurs more apparently.There also exists the downstream effect of UHI in Nanjing.
Abstract:
Based on the multiple type observational data,this paper preliminarily analyses the meso scale convective systems(MCSs) and weather background producing an extremely heavy rain along the Mei yu front in Hubei and Anhui provinces during 29—30 June 2009,and investigates the multi scale structure features of the Mei yu frontal rainstorm system.Then the meso scale numerical model WRF with large domain and 9 km horizontal resolution is used to carry out a 3 domain nested fine simulation for the heavy rain process.Morlet wavelet transformation is carried out to do spatial band passing filter for the model outputs,and the meso 〖WTBX〗α, β〖WTB1〗 and 〖WTBX〗γ〖WTB1〗 scale systems are separated out,in such a way that the three dimensional spatial dynamic and thermodynamic characteristics of the meso scale systems with different scales are studied.The results are as follows.The extremely Mei yu frontal heavy rain is directly resulted from several MCSs with different scales,which are of different features on satellite cloud images and radar echoes.On meso 〖WTBX〗α, β〖WTB1〗 and 〖WTBX〗γ〖WTB1〗 scales,the Mei yu frontal heavy rain system has obvious different dynamic and thermodynamic structure features in horizontal and vertical directions.The meso 〖WTBX〗α〖WTB1〗 and 〖WTBX〗β〖WTB1〗 scale systems have obvious vertical circulation,while meso 〖WTBX〗γ〖WTB1〗 scale system has some features of inertial gravity waves and usually develops in meso 〖WTBX〗α〖WTB1〗 and 〖WTBX〗β〖WTB1〗 scale system.Lastly,a physic conceptual model is advanced for the typical Mei yu frontal rainstorm system.
Based on the multiple type observational data,this paper preliminarily analyses the meso scale convective systems(MCSs) and weather background producing an extremely heavy rain along the Mei yu front in Hubei and Anhui provinces during 29—30 June 2009,and investigates the multi scale structure features of the Mei yu frontal rainstorm system.Then the meso scale numerical model WRF with large domain and 9 km horizontal resolution is used to carry out a 3 domain nested fine simulation for the heavy rain process.Morlet wavelet transformation is carried out to do spatial band passing filter for the model outputs,and the meso 〖WTBX〗α, β〖WTB1〗 and 〖WTBX〗γ〖WTB1〗 scale systems are separated out,in such a way that the three dimensional spatial dynamic and thermodynamic characteristics of the meso scale systems with different scales are studied.The results are as follows.The extremely Mei yu frontal heavy rain is directly resulted from several MCSs with different scales,which are of different features on satellite cloud images and radar echoes.On meso 〖WTBX〗α, β〖WTB1〗 and 〖WTBX〗γ〖WTB1〗 scales,the Mei yu frontal heavy rain system has obvious different dynamic and thermodynamic structure features in horizontal and vertical directions.The meso 〖WTBX〗α〖WTB1〗 and 〖WTBX〗β〖WTB1〗 scale systems have obvious vertical circulation,while meso 〖WTBX〗γ〖WTB1〗 scale system has some features of inertial gravity waves and usually develops in meso 〖WTBX〗α〖WTB1〗 and 〖WTBX〗β〖WTB1〗 scale system.Lastly,a physic conceptual model is advanced for the typical Mei yu frontal rainstorm system.
2023,46(3): 332-344, DOI: 10.13878/j.cnki.dqkxxb.20230303001
Abstract:
The summer of 2022 exhibits significant characteristics of high temperature,low humidity,and rainfall in South China.Previous studies have focused on extreme events of high temperature and low rainfall in summer,whereas attention to near-ground relative humidity,which is closely related to human comfort and crop growth,has been relatively insufficient.In this study,we define events of positive temperature anomaly,negative precipitation anomaly,and negative relative humidity anomaly exceeding one time of the interannual standard deviation between 1959 and 2022 are as compound events of summer high temperature,low humidity,and rainfall.Monthly ERA5 atmospheric reanalysis data of 1959—2022 are used in this study.We study the effect of spring soil moisture on the compound events in summer by composite analysis and a dynamic adjustment approach based on constructed circulation analogs,and the physical mechanism is analyzed.The results show that:1) The hot spots of the coupling between spring soil moisture and summer climate in south China are basically consistent with the high variability of summer temperature,precipitation,and relative humidity in 2022.2) When the soil in the Yangtze River Basin and Huang-Huai area is dry in spring and the southeast area is wet,the compound events of drying and heat will occur in summer.3) The effect of spring soil moisture on summer climate variability is mainly realized by adjusting the distribution of local evapotranspiration and net radiation energy.The study of the compound extreme events of high temperature,low humidity,and rainfall is of great significance in effectively preventing all kinds of disasters and safety accidents caused by them,protecting people's lives and property,and maintaining social production order.
The summer of 2022 exhibits significant characteristics of high temperature,low humidity,and rainfall in South China.Previous studies have focused on extreme events of high temperature and low rainfall in summer,whereas attention to near-ground relative humidity,which is closely related to human comfort and crop growth,has been relatively insufficient.In this study,we define events of positive temperature anomaly,negative precipitation anomaly,and negative relative humidity anomaly exceeding one time of the interannual standard deviation between 1959 and 2022 are as compound events of summer high temperature,low humidity,and rainfall.Monthly ERA5 atmospheric reanalysis data of 1959—2022 are used in this study.We study the effect of spring soil moisture on the compound events in summer by composite analysis and a dynamic adjustment approach based on constructed circulation analogs,and the physical mechanism is analyzed.The results show that:1) The hot spots of the coupling between spring soil moisture and summer climate in south China are basically consistent with the high variability of summer temperature,precipitation,and relative humidity in 2022.2) When the soil in the Yangtze River Basin and Huang-Huai area is dry in spring and the southeast area is wet,the compound events of drying and heat will occur in summer.3) The effect of spring soil moisture on summer climate variability is mainly realized by adjusting the distribution of local evapotranspiration and net radiation energy.The study of the compound extreme events of high temperature,low humidity,and rainfall is of great significance in effectively preventing all kinds of disasters and safety accidents caused by them,protecting people's lives and property,and maintaining social production order.
2019,42(4): 631-640, DOI: 10.13878/j.cnki.dqkxxb.20170815015
Abstract:
Imperative quality control methods for Doppler radar data,such as ground clutter elimination,range folding elimination and velocity dealiasing,should be adopted before being used for quantitative analyses,due to the serious impacts originating from certain non-meteorological factors.In this study,in order to precisely identify the ground clutter and precipitous echo,an automatic algorithm based on the Support Vector Machine(SVM) is performed,based on the observational CINRAD/SA Doppler weather radar data in the areas of Anqing and Changzhou from June to August,2013,and the results are compared with the recognition effect based on the Artificial Neural Networks(ANNs) method.Statistical learning theory(SLT) is favorable for small samples,which focuses on the statistical law and nature of small-sample learning.As a new machine learning based on SLT,the basic principle of the SVM is to possess an optimal separating hyperplane which is able to satisfy the requirement of the classification accuracy by introducing the largest classification intervals on either side of the hyperplane.In the first step,the dataset used in the experiment will be establised by empirically distinguishing the ground clutter and precipitous points at each bin.Next,several characteristic parameters,which are used to quantify the possibility affected by the ground clutter,such as reflectivity vertical variation (GDBZ),reflectivity horizontal texture (TDBZ),velocity regional average (MDVE),and spectrum regional average (MDSW),will be derived from the reflectivity,radaial velocity,spectrum width and spatial variance information of the ground clutter and precipitous echo.The statistical results of the above characteristic parameters show the following:a large portion of these parameters vary in terms of ground clutter and precipitous echo,which indicates that the seven parameters (GDBZ,TDBZ,SPIN,SIGN,MDVE,MDSW and SDVE) contribute to the identifiable recognition of the ground clutter and precipitous echo.Based on the above conclusions,seven parameters,which are regarded as the trigger (the training factor of SVM) to establish the SVM's training model,can be randomly extracted from the database.Finally,the training model is used to automatically recognize the ground clutter and precipitation using the random data from the database.The recognition effect of the SVM method will be examined by comparing the model output with the empirical identifications,and the examination of the ANNs algorithm is the same as that of the SVM method.The comparison of the recognition effect between the SVM and ANNs methods reveals the following:(1) The statistically identifiable recognition parameter for the sSVM and ANNs methods appears to be steady,despite the fact that the Doppler radar data vary in shape and position between Anqing and Changzhou;(2) An identifying threshold must be determined for the ANNs method before the ground clutter and precipitous echo are identified,which will lead to a differently identifiable accuracy with the unlike threshold;and (3) Overall,the SVM method works better than the ANNs method in terms of radar echo identification.Moreover,the identifiable recognition accuracy of the latter increases significantly with the increasing total number of training samples,while the identifiable recognition accuracy of the former performs at a highly accurate level,which remains relatively stable with the changes in the training samples.In terms of the identification accuracy of the total samples (ground clutter and precipitous echo) and identification accuracy of the ground clutter echo,the SVM method presents better results than the ANNs method.As for the precipitous echo erroneous recognition,the ANNs method performs slightly better than the SVM,but both methods control the erroneous recognition rate at a low level.
Imperative quality control methods for Doppler radar data,such as ground clutter elimination,range folding elimination and velocity dealiasing,should be adopted before being used for quantitative analyses,due to the serious impacts originating from certain non-meteorological factors.In this study,in order to precisely identify the ground clutter and precipitous echo,an automatic algorithm based on the Support Vector Machine(SVM) is performed,based on the observational CINRAD/SA Doppler weather radar data in the areas of Anqing and Changzhou from June to August,2013,and the results are compared with the recognition effect based on the Artificial Neural Networks(ANNs) method.Statistical learning theory(SLT) is favorable for small samples,which focuses on the statistical law and nature of small-sample learning.As a new machine learning based on SLT,the basic principle of the SVM is to possess an optimal separating hyperplane which is able to satisfy the requirement of the classification accuracy by introducing the largest classification intervals on either side of the hyperplane.In the first step,the dataset used in the experiment will be establised by empirically distinguishing the ground clutter and precipitous points at each bin.Next,several characteristic parameters,which are used to quantify the possibility affected by the ground clutter,such as reflectivity vertical variation (GDBZ),reflectivity horizontal texture (TDBZ),velocity regional average (MDVE),and spectrum regional average (MDSW),will be derived from the reflectivity,radaial velocity,spectrum width and spatial variance information of the ground clutter and precipitous echo.The statistical results of the above characteristic parameters show the following:a large portion of these parameters vary in terms of ground clutter and precipitous echo,which indicates that the seven parameters (GDBZ,TDBZ,SPIN,SIGN,MDVE,MDSW and SDVE) contribute to the identifiable recognition of the ground clutter and precipitous echo.Based on the above conclusions,seven parameters,which are regarded as the trigger (the training factor of SVM) to establish the SVM's training model,can be randomly extracted from the database.Finally,the training model is used to automatically recognize the ground clutter and precipitation using the random data from the database.The recognition effect of the SVM method will be examined by comparing the model output with the empirical identifications,and the examination of the ANNs algorithm is the same as that of the SVM method.The comparison of the recognition effect between the SVM and ANNs methods reveals the following:(1) The statistically identifiable recognition parameter for the sSVM and ANNs methods appears to be steady,despite the fact that the Doppler radar data vary in shape and position between Anqing and Changzhou;(2) An identifying threshold must be determined for the ANNs method before the ground clutter and precipitous echo are identified,which will lead to a differently identifiable accuracy with the unlike threshold;and (3) Overall,the SVM method works better than the ANNs method in terms of radar echo identification.Moreover,the identifiable recognition accuracy of the latter increases significantly with the increasing total number of training samples,while the identifiable recognition accuracy of the former performs at a highly accurate level,which remains relatively stable with the changes in the training samples.In terms of the identification accuracy of the total samples (ground clutter and precipitous echo) and identification accuracy of the ground clutter echo,the SVM method presents better results than the ANNs method.As for the precipitous echo erroneous recognition,the ANNs method performs slightly better than the SVM,but both methods control the erroneous recognition rate at a low level.
Abstract:
Wind shear in the atmosphere is a serious threat to the safety of aircraft,especially the low-level wind shear which is an important factor affecting the aircraft taking off and landing.By using the Doppler radar velocity data to calculate the one-dimension tangential,one-dimensional radial and two-dimension composite shear,accurately judging the dangerous area of wind shear could provide timely warning for flight,taking off and landing.In this study,as the wind shear automatic identification product on the principal user processor(PUP) for Doppler radar applications has the shortcomings such as weak edge recognition and larger location errors,according to Doppler radar velocity distributions and taking advantage of least square fitting method,"fitting window" suitable for airborne radar parameters are chosen,and the several cases have been identified and analyzed.For the performance in wind shear's identification,location and edge discerning,the least square method could provide better reference of wind shear and warnings than PUP's identification products.
Wind shear in the atmosphere is a serious threat to the safety of aircraft,especially the low-level wind shear which is an important factor affecting the aircraft taking off and landing.By using the Doppler radar velocity data to calculate the one-dimension tangential,one-dimensional radial and two-dimension composite shear,accurately judging the dangerous area of wind shear could provide timely warning for flight,taking off and landing.In this study,as the wind shear automatic identification product on the principal user processor(PUP) for Doppler radar applications has the shortcomings such as weak edge recognition and larger location errors,according to Doppler radar velocity distributions and taking advantage of least square fitting method,"fitting window" suitable for airborne radar parameters are chosen,and the several cases have been identified and analyzed.For the performance in wind shear's identification,location and edge discerning,the least square method could provide better reference of wind shear and warnings than PUP's identification products.
2023,46(6): 950-960, DOI: 10.13878/j.cnki.dqkxxb.20230313001
Abstract:
Northern China experienced four sandstorms or severe sandstorms in spring 2021, contrasting with just one event in the corresponding period of 2022. Utilizing air quality and multi-source meteorological data spanning 2015 to 2022, we applied the Lamb Jenkinson classification and Mann-Whitney U test methods to analyze similarities and differences in the sand source areas' conditions and meteorological factors during the spring of 2021 and 2022. Our findings reveal that the sand and dust weather (SDW) in northern China is frequently categorized into NW-N (cyclone type) and E-NE (high-pressure type), with the NW-N type leading to higher PM10 extreme values and a broader range of high concentrations. In terms of meteorological factors, synoptic conditions favorable for SDW in spring 2022 occur more frequently, with the differences in daily PM10 concentration predominantly associated with the NW-N type when compared to spring 2021. The frequency of NW-N type events and cyclone intensity remains comparable between the two periods, along with similar dynamic uplift conditions conducive to SDW are similar. Regarding sand source area conditions, the soil temperature in Mongolia's sand source area displayed a “cold before and warm after” pattern in the pre-winter of 2021, resulting in an early peak of snowmelt and other water content. In addition, a widespread decrease in precipitation and a relatively strong cyclone in Mongolia's sand source area in March contributed to the high incidence of sand and dust in spring 2021. Conversely, during the pre-winter of 2022, the soil temperature in Mongolia's sand source area followed a “warm before and cold after” trend, leading to a delayed peak of water content and soil moisture content during the snowmelt period. These conditions, characterized by thicker and moisture soil, were less conducive to sand formation. Therefore, the disparities in Mongolian sand source area conditions represent the primary factor behind the significant differences in SDW between the two periods.
Northern China experienced four sandstorms or severe sandstorms in spring 2021, contrasting with just one event in the corresponding period of 2022. Utilizing air quality and multi-source meteorological data spanning 2015 to 2022, we applied the Lamb Jenkinson classification and Mann-Whitney U test methods to analyze similarities and differences in the sand source areas' conditions and meteorological factors during the spring of 2021 and 2022. Our findings reveal that the sand and dust weather (SDW) in northern China is frequently categorized into NW-N (cyclone type) and E-NE (high-pressure type), with the NW-N type leading to higher PM10 extreme values and a broader range of high concentrations. In terms of meteorological factors, synoptic conditions favorable for SDW in spring 2022 occur more frequently, with the differences in daily PM10 concentration predominantly associated with the NW-N type when compared to spring 2021. The frequency of NW-N type events and cyclone intensity remains comparable between the two periods, along with similar dynamic uplift conditions conducive to SDW are similar. Regarding sand source area conditions, the soil temperature in Mongolia's sand source area displayed a “cold before and warm after” pattern in the pre-winter of 2021, resulting in an early peak of snowmelt and other water content. In addition, a widespread decrease in precipitation and a relatively strong cyclone in Mongolia's sand source area in March contributed to the high incidence of sand and dust in spring 2021. Conversely, during the pre-winter of 2022, the soil temperature in Mongolia's sand source area followed a “warm before and cold after” trend, leading to a delayed peak of water content and soil moisture content during the snowmelt period. These conditions, characterized by thicker and moisture soil, were less conducive to sand formation. Therefore, the disparities in Mongolian sand source area conditions represent the primary factor behind the significant differences in SDW between the two periods.
2016,39(6): 722-734, DOI: 10.13878/j.cnki.dqkxxb.20161028003
Abstract:
The present paper has mainly analysed the process and mechanisms of genesis and development of the 2014-2016 mega El Niño event.It is shown that the entire lifecycle of the event is about 2 years(from April 2014 to May 2016),with four stages identified for its evolutive process:(1)Early and continuous westerly wind bursts(December 2013 to April 2014).The continuous three westerly wind burstsnot only changed the state of the easterly trade wind prevailing tropical central and easterly in the Pacific for long period of time,but also changed the cold water state in this region for the most recent 12 years,thus leading to SST rise and warming.Until early spring 2014,the SSTA exceeded 0.5℃,marking the possible occurrence of a new El Niño event.(2)Alternative weakening period(June 2014 to August 2015).Six westerly wind bursts continued to occur,thus maintaining and enhancing the warming of the equatorial central and eastern Pacific,while at the same time overcoming two periods of SST warming decrease or barrier,so that the initial development of El Niño was not aborted,and even changed into the stage of strong El Niño.Correspondingly,in the sub layer of the equatorial central and eastern Pacific,six warm Kelvin waves were observed to propagate eastward.The heat contents of these oceanic waves not only maintained the continuous warming in the equatorial central and eastern Pacific,but also caused El Niño to change from CP to EP type.(3)Peak period of development (September 2015 to February 2016).Two stronger westerly wind bursts were observed,which corresponded to very vigorous convective activity on the equatorial central and eastern Pacific.Rapid warming occurred in the Niño3.4 region,with 3℃observed in November 2015,classified as the mega-El Niño event.(4)Accelerating weakening stage(March to May 2016).The intensity of the El Niño rapidly weakened from 2 to 0.5℃ in the Niño3.4 region,then accelerated the transition to the cold water phase.In July to August 2016,the SSTA in the Niño3.4 region already approached -0.5℃.This rapid phase shift is a manifestation of the theory of delayed oscillation.From the above results,it is concluded that the development and shift of warm and cold phases is observationally consistent with the mechanism derived from the paradigm of the current theory of recharge oscillation and/or delayed oscillation theory.This clearly demonstrates that the results of the El Niño theory effectively underpin the development of related operational prediction.
The present paper has mainly analysed the process and mechanisms of genesis and development of the 2014-2016 mega El Niño event.It is shown that the entire lifecycle of the event is about 2 years(from April 2014 to May 2016),with four stages identified for its evolutive process:(1)Early and continuous westerly wind bursts(December 2013 to April 2014).The continuous three westerly wind burstsnot only changed the state of the easterly trade wind prevailing tropical central and easterly in the Pacific for long period of time,but also changed the cold water state in this region for the most recent 12 years,thus leading to SST rise and warming.Until early spring 2014,the SSTA exceeded 0.5℃,marking the possible occurrence of a new El Niño event.(2)Alternative weakening period(June 2014 to August 2015).Six westerly wind bursts continued to occur,thus maintaining and enhancing the warming of the equatorial central and eastern Pacific,while at the same time overcoming two periods of SST warming decrease or barrier,so that the initial development of El Niño was not aborted,and even changed into the stage of strong El Niño.Correspondingly,in the sub layer of the equatorial central and eastern Pacific,six warm Kelvin waves were observed to propagate eastward.The heat contents of these oceanic waves not only maintained the continuous warming in the equatorial central and eastern Pacific,but also caused El Niño to change from CP to EP type.(3)Peak period of development (September 2015 to February 2016).Two stronger westerly wind bursts were observed,which corresponded to very vigorous convective activity on the equatorial central and eastern Pacific.Rapid warming occurred in the Niño3.4 region,with 3℃observed in November 2015,classified as the mega-El Niño event.(4)Accelerating weakening stage(March to May 2016).The intensity of the El Niño rapidly weakened from 2 to 0.5℃ in the Niño3.4 region,then accelerated the transition to the cold water phase.In July to August 2016,the SSTA in the Niño3.4 region already approached -0.5℃.This rapid phase shift is a manifestation of the theory of delayed oscillation.From the above results,it is concluded that the development and shift of warm and cold phases is observationally consistent with the mechanism derived from the paradigm of the current theory of recharge oscillation and/or delayed oscillation theory.This clearly demonstrates that the results of the El Niño theory effectively underpin the development of related operational prediction.
2021,44(1): 39-49, DOI: 10.13878/j.cnki.dqkxxb.20201113007
Abstract:
The Arctic climate,an important component of the global climate system,has moved into a new state over the past 20 years.Scientific questions and possible consequences related to these changes are now front in the midst of many important issues that the world needs to deal with in the future.These changes,including prominent atmospheric and oceanic warming and sea ice melting have been largely attributed to a combined effect of anthropogenic forcing and internal variability of the climate system.This review highlights some findings from a number of studies conducted by my research group in the past few years.The studies collectively suggest that the high latitude atmospheric circulation that is sensitive to tropical SST forcing related to the interdecadal Pacific oscillation (IPO) plays a vital role in driving the interannual and interdecadal variability of Arctic sea ice by affecting the atmospheric temperature,moisture,clouds and radiative fluxes over sea ice.In particular,the teleconnection excited by a SST cooling over the tropical Pacific is suggested to cause an enhanced melting from 2007 to 2012.In addition,it suggests that a similar internal process may also play a role to cause strong sea ice melting in summer 2020.Furthermore,the model evaluation focusing on CMIP5 models finds that most climate models have a limitation to replicate this IPO-related teleconnection,raising awareness on an urgent need to investigate the cause of this bias in models.Thus,this review is meant to offer priorities for future Arctic research so that more efforts are targeted on critical scientific questions raised in this study.
The Arctic climate,an important component of the global climate system,has moved into a new state over the past 20 years.Scientific questions and possible consequences related to these changes are now front in the midst of many important issues that the world needs to deal with in the future.These changes,including prominent atmospheric and oceanic warming and sea ice melting have been largely attributed to a combined effect of anthropogenic forcing and internal variability of the climate system.This review highlights some findings from a number of studies conducted by my research group in the past few years.The studies collectively suggest that the high latitude atmospheric circulation that is sensitive to tropical SST forcing related to the interdecadal Pacific oscillation (IPO) plays a vital role in driving the interannual and interdecadal variability of Arctic sea ice by affecting the atmospheric temperature,moisture,clouds and radiative fluxes over sea ice.In particular,the teleconnection excited by a SST cooling over the tropical Pacific is suggested to cause an enhanced melting from 2007 to 2012.In addition,it suggests that a similar internal process may also play a role to cause strong sea ice melting in summer 2020.Furthermore,the model evaluation focusing on CMIP5 models finds that most climate models have a limitation to replicate this IPO-related teleconnection,raising awareness on an urgent need to investigate the cause of this bias in models.Thus,this review is meant to offer priorities for future Arctic research so that more efforts are targeted on critical scientific questions raised in this study.
2015,38(1): 27-36, DOI: 10.13878/j.cnki.dqkxxb.20130626001
Abstract:
The high-resolution numerical simulations of Hurricane Bonnie(1998) are used to analyze its intensity and structure changes in relation to its associated inertial stability under the influence of intense vertical wind shear during three different stages of its life cycle.Results show that Bonnie has high asymmetry and experiences an eyewall displacement cycle during its rapid intensifying stage.During its rapid structure change stage,the development of high inertial stability is consistent with the change in hurricane inner core size.The inertially stable region,which is usually present inside the eyewall,provides resistance to radial motions,and plays an important role in reducing the influence of vertical wind shear.The inertially stable region reduces the Rossby radius of deformation,and concentrates the latent heating,which is beneficial to the enhancing of the hurricane.This is an important factor in the development of inner core region of the hurricane.
The high-resolution numerical simulations of Hurricane Bonnie(1998) are used to analyze its intensity and structure changes in relation to its associated inertial stability under the influence of intense vertical wind shear during three different stages of its life cycle.Results show that Bonnie has high asymmetry and experiences an eyewall displacement cycle during its rapid intensifying stage.During its rapid structure change stage,the development of high inertial stability is consistent with the change in hurricane inner core size.The inertially stable region,which is usually present inside the eyewall,provides resistance to radial motions,and plays an important role in reducing the influence of vertical wind shear.The inertially stable region reduces the Rossby radius of deformation,and concentrates the latent heating,which is beneficial to the enhancing of the hurricane.This is an important factor in the development of inner core region of the hurricane.
2020,43(4): 663-672, DOI: 10.13878/j.cnki.dqkxxb.20190330001
Abstract:
In this paper,using conventional observation data,NCEP 1°×1° reanalysis data,FY-2G satellite hourly TBB data,radar and AWS data,the potential and triggering characteristics of short-term heavy precipitation in southeastern Shanxi Province on the night of July 13,2018 were analyzed.The results reveal that the strong southwest airflow around subtropical high provide abundant water vapor conditions for the short-term heavy precipitation process.In addition,the stratification structure of "dry and cold under warm and wet" and the temperature differential advection of "high-level cold advection and low-level warm advection" provides the energy conditions required for the development of strong convection.The formation and maintenance of ascending motion are conducive to the release and enhancement of unstable energy.The meso-β scale convergence line on the ground develops into a meso-β scale vortex,thereby stimulating the consolidation and strengthening of the mid-β scale convective cloud mass,which in turn stimulates the merging and strengthening of meso-βscale convective clouds.The meso-γscale convective monomer embedded in the meso-βscale band echo of ≥ 35 dBZ,under the guidance of the 500 hPa southwest airflow,forms a slowly moving,highly organized multi-cell linear echo,which was the direct cause of the formation of short-duration heavy rainfall.The short-term heavy precipitation is located between 5 880 gpm and 5 840 gpm on the 500 hPa map,between the 850 hPa and 700 hPa shear line,and overlaps with 850 hPa and 700 hPa wet tongue,ground trunk line and mesoscale convergence line (near the 10 km range),as well as the cold air inflow side of the convective cloud mass TBB gradient high value area and TBB ≤ -60℃.
In this paper,using conventional observation data,NCEP 1°×1° reanalysis data,FY-2G satellite hourly TBB data,radar and AWS data,the potential and triggering characteristics of short-term heavy precipitation in southeastern Shanxi Province on the night of July 13,2018 were analyzed.The results reveal that the strong southwest airflow around subtropical high provide abundant water vapor conditions for the short-term heavy precipitation process.In addition,the stratification structure of "dry and cold under warm and wet" and the temperature differential advection of "high-level cold advection and low-level warm advection" provides the energy conditions required for the development of strong convection.The formation and maintenance of ascending motion are conducive to the release and enhancement of unstable energy.The meso-β scale convergence line on the ground develops into a meso-β scale vortex,thereby stimulating the consolidation and strengthening of the mid-β scale convective cloud mass,which in turn stimulates the merging and strengthening of meso-βscale convective clouds.The meso-γscale convective monomer embedded in the meso-βscale band echo of ≥ 35 dBZ,under the guidance of the 500 hPa southwest airflow,forms a slowly moving,highly organized multi-cell linear echo,which was the direct cause of the formation of short-duration heavy rainfall.The short-term heavy precipitation is located between 5 880 gpm and 5 840 gpm on the 500 hPa map,between the 850 hPa and 700 hPa shear line,and overlaps with 850 hPa and 700 hPa wet tongue,ground trunk line and mesoscale convergence line (near the 10 km range),as well as the cold air inflow side of the convective cloud mass TBB gradient high value area and TBB ≤ -60℃.
Abstract:
利用IAEA\WMO\GNIP的降水稳定同位素资料，分析了中国降水稳定同位素的时空分布特征及其影响因素。结果表明，整体来看我国降水稳定同位素有明显的大陆效应和高度效应。各地大气降水线存在地域差异，内陆地区同一站点冬、夏半年也有明显差异，显示出水汽团特性的不同。不同地区降水稳定同位素（δ和过量氘）的季节变化特征明显不同，表明主要水汽来源存在季节性差异。通过对比长序列降水稳定同位素的年际变化与季风和ENSO指数的关系，发现ENSO与降水稳定同位素有显著的正相关，但不一定通过影响降水量来引起降水稳定同位素（stable isotope in precipitation, SIP）的变化。重点分析了我国降水量效应、温度效应的特点，指出沿海和西南等季风区主要受降水量的影响，北方非季风区温度效应起主要作用，交叉地带则两种效应都有影响。
利用IAEA\WMO\GNIP的降水稳定同位素资料，分析了中国降水稳定同位素的时空分布特征及其影响因素。结果表明，整体来看我国降水稳定同位素有明显的大陆效应和高度效应。各地大气降水线存在地域差异，内陆地区同一站点冬、夏半年也有明显差异，显示出水汽团特性的不同。不同地区降水稳定同位素（δ和过量氘）的季节变化特征明显不同，表明主要水汽来源存在季节性差异。通过对比长序列降水稳定同位素的年际变化与季风和ENSO指数的关系，发现ENSO与降水稳定同位素有显著的正相关，但不一定通过影响降水量来引起降水稳定同位素（stable isotope in precipitation, SIP）的变化。重点分析了我国降水量效应、温度效应的特点，指出沿海和西南等季风区主要受降水量的影响，北方非季风区温度效应起主要作用，交叉地带则两种效应都有影响。
JIANG Tong, ZHAI Jianqing, LUO Yong, SU Buda, CHAO Qingchen, WANG Yanjun, WANG Guojie, HUANG Jinlong, XU Runhong, GAO Miaoni, MIAO Lijuan
2022,45(4): 502-511, DOI: 10.13878/j.cnki.dqkxxb.20220529013
Abstract:
The second working group of the IPCC Sixth Assessment Report (IPCC AR6 WGⅡ) focuses on the impact,risk,adaptation and vulnerability of climate change.The report quantitatively assesses the impact of climate change on natural and human systems with the latest data,detailed evidence and diverse methods.Compared to AR5,the following progress has been made:Firstly,The content clarifies that the impact of climate change is attributable to three categories:anthropogenic climate forcing,non-climate factor action and weather sensitivity identification,127 key risks from climate change will become widespread or irreversible,and limiting global warming to 1.5 ℃ can greatly reduce climate change loss and damage to natural and human systems,pointing to the importance of adapting to transition.Secondly,AR6 WGⅡ adopts the latest combination of SSPs and RCPS in terms of evaluation method,which is more comprehensive.Thirdly,AR6 WGⅡ has focus on risks and solutions,and on the basis of AR5 WGⅡ,it is clarified that under different future warming scenarios,the risk level of the key risks facing the five “reasons for concern (RFCs)” will be relied on lower to very high levels of global warming.Finally,AR6 WGⅡ clarifies the urgency of climate action,combining adaptation and mitigation to support sustainable development is essential for climate resilience development pathways,pointing to the importance of immediate action to address climate risks.
The second working group of the IPCC Sixth Assessment Report (IPCC AR6 WGⅡ) focuses on the impact,risk,adaptation and vulnerability of climate change.The report quantitatively assesses the impact of climate change on natural and human systems with the latest data,detailed evidence and diverse methods.Compared to AR5,the following progress has been made:Firstly,The content clarifies that the impact of climate change is attributable to three categories:anthropogenic climate forcing,non-climate factor action and weather sensitivity identification,127 key risks from climate change will become widespread or irreversible,and limiting global warming to 1.5 ℃ can greatly reduce climate change loss and damage to natural and human systems,pointing to the importance of adapting to transition.Secondly,AR6 WGⅡ adopts the latest combination of SSPs and RCPS in terms of evaluation method,which is more comprehensive.Thirdly,AR6 WGⅡ has focus on risks and solutions,and on the basis of AR5 WGⅡ,it is clarified that under different future warming scenarios,the risk level of the key risks facing the five “reasons for concern (RFCs)” will be relied on lower to very high levels of global warming.Finally,AR6 WGⅡ clarifies the urgency of climate action,combining adaptation and mitigation to support sustainable development is essential for climate resilience development pathways,pointing to the importance of immediate action to address climate risks.
2014,37(5): 653-664, DOI: 10.13878/j.cnki.dqkxxb.20111230001
Abstract:
Studies have shown that large-scale monsoon gyre activity is closely associated with tropical cyclogenesis over the western North Pacific.In this study,two cases of monsoon gyre activities in 2002 and 2009 were first examined.It was found that a monsoon gyre can be linked to the formation of one or more tropical cyclones,which usually occur near or to the east of the gyre center.Further analysis of the monsoon gyre activity during the period of 2000—2009 indicates that tropical cyclogenesis mainly occurs near or to the east of the gyre center,although the definition of a monsoon gyre depends on its duration and the circulation intensity.It is suggested that the tropical cyclogensis may be associated with the Rossby wave energy dispersion of monsoon gyres.
Studies have shown that large-scale monsoon gyre activity is closely associated with tropical cyclogenesis over the western North Pacific.In this study,two cases of monsoon gyre activities in 2002 and 2009 were first examined.It was found that a monsoon gyre can be linked to the formation of one or more tropical cyclones,which usually occur near or to the east of the gyre center.Further analysis of the monsoon gyre activity during the period of 2000—2009 indicates that tropical cyclogenesis mainly occurs near or to the east of the gyre center,although the definition of a monsoon gyre depends on its duration and the circulation intensity.It is suggested that the tropical cyclogensis may be associated with the Rossby wave energy dispersion of monsoon gyres.
Abstract:
A new atmospheric correction algorithm based on dark object method and the look up table developed from MODTRAN model was introduced for Landsat images in the paper.The infomation of the satellite remote sensing images was used to support the atmospheric correction.The algorithm was applied to the Landsat ETM+imagery and comparisons show that the influence on Landsat imagery caused by molecules,water vapor,ozone,and aerosol particles in the atmosphere was effectively reduced after the correction.The surface reflectivity was more precisely,which is beneficial for remote sensing information extraction and thematic interpretation.
A new atmospheric correction algorithm based on dark object method and the look up table developed from MODTRAN model was introduced for Landsat images in the paper.The infomation of the satellite remote sensing images was used to support the atmospheric correction.The algorithm was applied to the Landsat ETM+imagery and comparisons show that the influence on Landsat imagery caused by molecules,water vapor,ozone,and aerosol particles in the atmosphere was effectively reduced after the correction.The surface reflectivity was more precisely,which is beneficial for remote sensing information extraction and thematic interpretation.
2015,38(2): 184-194, DOI: 10.13878/j.cnki.dqkxxb.20140508002
Abstract:
The observed SST data and CMIP5 data are used to analyze climate state and interdecadal variation of sea surface temperature(SST) over Northwest Pacific(20—60°N,120°E—120°W).Results indicate that the selected 22 models can simulate the climate state perfectly.More importantly,the selected models can simulate the annual and interdecadal variations of SST over Northwest Pacific.Total standard deviation of SST simulted by the models is the largest in Kuroshio extension region.The majority of models have an ability to simulate the first EOF mode of SST.The SST over Northwest Pacific has a significant interdecadal oscillation phenomenon.SSTs simulated by the 13/22 models have obvious interdecadal oscillations.Meanwhile,the simulated deviation of SST climate state has a great effect on the periodic oscillation of SST,especially in Kuroshio extension region.
The observed SST data and CMIP5 data are used to analyze climate state and interdecadal variation of sea surface temperature(SST) over Northwest Pacific(20—60°N,120°E—120°W).Results indicate that the selected 22 models can simulate the climate state perfectly.More importantly,the selected models can simulate the annual and interdecadal variations of SST over Northwest Pacific.Total standard deviation of SST simulted by the models is the largest in Kuroshio extension region.The majority of models have an ability to simulate the first EOF mode of SST.The SST over Northwest Pacific has a significant interdecadal oscillation phenomenon.SSTs simulated by the 13/22 models have obvious interdecadal oscillations.Meanwhile,the simulated deviation of SST climate state has a great effect on the periodic oscillation of SST,especially in Kuroshio extension region.
Abstract:
超级单体风暴常伴随着冰雹、雷雨大风等强对流天气，最本质的特征是有一持久深厚的几千米尺度的涡旋———中气旋。利用2003-2009年福建龙岩新一代天气雷达观测到的32次超级单体风暴，分析了超级单体风暴中气旋的时空分布、结构特征以及旋转速度大小、中气旋顶和底的高度、伸长厚度以及切变值等特征量。结果表明：90%以上的超级单体中尺度气旋是与冰雹、雷雨大风、短时强降水等强对流天气相联系的。统计8次有详细灾情的雷雨大风或冰雹天气过程发现，中气旋强度不断加强，中气旋厚度加大，最强切变中心突降时将产生大风或冰雹等强对流天气
超级单体风暴常伴随着冰雹、雷雨大风等强对流天气，最本质的特征是有一持久深厚的几千米尺度的涡旋———中气旋。利用2003-2009年福建龙岩新一代天气雷达观测到的32次超级单体风暴，分析了超级单体风暴中气旋的时空分布、结构特征以及旋转速度大小、中气旋顶和底的高度、伸长厚度以及切变值等特征量。结果表明：90%以上的超级单体中尺度气旋是与冰雹、雷雨大风、短时强降水等强对流天气相联系的。统计8次有详细灾情的雷雨大风或冰雹天气过程发现，中气旋强度不断加强，中气旋厚度加大，最强切变中心突降时将产生大风或冰雹等强对流天气
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Ranking List
- 1APPLICATION OF WAVELET TRANSFORMATION TO ANALYSIS OF MULTIPLE TIME SCALE CLIMATE CHANGE
- 2Major Achievements in Meteorology in Ancient China
- 3CMIP5 projected changes in mean and extreme climate in the Belt and Road region
- 4The impact of the Himalayan-Tibet Plateau on the monsoon region
- 5Discussion on calculation method of flowering date meeting of hybrid rice parents
- 6Quality assessment of hourly merged precipitation product over China
- 7Analysis and comparison of four kinds of circulation indices of Aleutian Low
- 8The differences in characteristics and applicability among three types of Rossby wave activity flux in atmospheric dynamics
- 9
- 10Multiscale structure features of a typical Meiyu frontal rainstorm process
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