Abstract:This paper presents the operation and evaluation outcomes of the “Sciences of Meteorology with Artificial-Intelligence in Research and Technology-Weather Forecast Demonstration Project for Beijing 2022 Winter Olympic and Paralympic Games,SMART2022-FDP” (referred to as FDP).The FDP marks the inaugural 0—10 d forecast demonstration project for winter weather in the mid-latitude mountains of China,involving 22 meteorological departments and institutions and with nearly 40 numerical models or objective forecasting systems participating.The FDP has effectively managed the transmission,distribution,monitoring,and real-time pre-processing of multi-source observations and output data products crucial for supporting real-time operations during the Games.Moreover,it has showcased real-time demonstration applications of innovative technologies such as sub-hundred-meter resolution forecasting and artificial intelligence-based forecasting in the complex mountainous competition venues.These efforts have resulted in the delivery of diverse and stable high-precision forecast products to the forecaster teams across the three Winter Olympics and Paralympics Divisions and various meteorological offices,thereby effectively supporting the provision of high-standard meteorological services for the games.A comprehensive evaluation of FDP products was conducted,revealing that specialized site post-processing forecasts exhibited significantly smaller forecast errors for meteorological elements such as average wind,gusts,temperature,and relative humidity compared to the original model forecasts.However,further improvements are needed in high-resolution numerical models and objective forecasting systems,particularly for precipitation and visibility forecasts.Additionally,the forecasting skill of site post-processing techniques was found to be less pronounced in the complex mountainous terrain during the winter.The optimal forecast verification indicators (minimum error values) for wind,temperature,precipitation,visibility,and relative humidity in FDP collectively underscore the existing capabilities of objective winter weather forecasting in mid-latitude,complex mountainous regions in China,offering valuable insights for addressing current challenges and enhancing high-resolution winter weather forecasts in such terrain.

Abstract:Under global warming, China is more vulnerable to the threat of extreme climate events. Studying the future climate change in China and providing more accurate future projections are of great significance for disaster prevention and mitigation, as well as policy-making in response to climate change. Based on the simulations of the global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we adopt a weighted scheme of Climate Model Weighting by Independence and Performance (ClimWIP) to carry out the multi-model ensemble constrain of mean and extreme temperature and precipitation over China region. Based on the performance evaluations of the constrained ensembles, the projected changes at the 1.5 and 2 ℃ global warming under the SSP5-8.5 scenario are studied. Results show that the ClimWIP scheme has better performance when compared to the unweighted scheme, which reduce the climatology bias of ensemble. The spatial correlation coefficient between temperature indices and observations exceeds 0.98, and standard deviation ratios are close to 1. The spatial correlation coefficient between total precipitation (PRCPTOT) and heavy precipitation (R95P) with observations exceeds 0.92 and standard deviation ratios between 0.8 and 1.0. The regions with higher projection uncertainty are mainly in Northern China and Tibetan Plateau for the temperature indices, and in North China and Northwest China for the precipitation indices. The projection uncertainty by the ClimWIP scheme is reduced when compared with the unweighted scheme. The reduction is greater for temperature indices in Southern China and Tibetan Plateau, while precipitation indices show a significant decreased in uncertainty in Northeast China and northwest Xinjiang. Under 2 ℃ global warming, the uncertainty of annual mean temperature (Tas), maximum temperature (TXx), and minimum temperature (TNn) in China is reduced by 19.2％, 22.1％, and 17.8％, respectively, while PRCPTOT and R95P is reduced 3.3％ and 4.7％, respectively. Regarding to the geographic distribution, ClimWIP scheme would see larger warming in Northern China and Tibetan Plateau for the temperature indices. More intense precipitation concentrate in Northwest China and Tibetan Plateau. Under an additional 0.5 ℃ global warming, the temperature response in China region is stronger than that of global response, with an average higher warming about 0.2 ℃. The response of TNn in parts of Northeast China even more than three times additional warming. And there would be an additional increase about 5.2％ and 10.5％ for PRCPTOT and R95P, respectively. From the perspective of probability projection, at the 2 ℃ global warming, the warming magnitude in most regions of China would be likely larger than 1.5 ℃ compared to the current climate state (probability value>50％), and the probability in parts of Northern China and Tibetan Plateau would be much higher (probability value>90％). For the precipitation indices, the probability of wetter condition in Northwest China and North China would be larger, with a likely response magnitude exceeding 10％, 25％ and -5 days for PRCPTOT and R95P and continuous dry days (CDD) (probability value>50％). The ClimWIP scheme can reduce the uncertainty of future projections and provide more accurate future projections, and more model evaluation metrics such as trends and key physical processes can be considered in ClimWIP scheme in the future. Alternatively, multi-modal large ensembles and high resolution models can be used to improve the reliability of future projections.

Abstract:Southeast Asia,characterized by intricate topography and dense population,is highly sensitive to precipitation extremes in the context of global warming,making understanding its future change characteristics essential.This study utilizes 26 global climate models (GCMs) to project changes in precipitation extremes over Southeast Asia by the end of the 21st century,based on the Shared Socioeconomic Pathway (SSP) scenarios 2-4.5 and 5-8.5 (SSP2-4.5 and SSP5-8.5) simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6).By decomposing the moisture budget equation,we quantify the relative contributions of increased atmospheric specific humidity and alterations in atmospheric circulation to climatological precipitation changes.The research explores both dynamic and thermodynamic mechanisms driving these precipitation changes,rigorously evaluating the consistency and reliability of simulation outputs across a diverse range of climate models.The multi-model ensemble medians (MMM) reveal that,compared with the historical reference period (1985—2014),climatological precipitation and precipitation extremes in most of Southeast Asia will increase significantly by the end of the 21st century (2071—2100) under SSP2-4.5 and SSP5-8.5 scenarios.Precipitation extremes exhibit spatial differences,with short duration,high intensity events predicted for Kalimantan,while southern Sumatra experiences a decrease in total precipitation on wet days (PRCPTOT) and an increase in consecutive dry days (CDD).Except for heavy precipitation days (R10mm),extreme precipitation indices show more pronounced magnitudes under the SSP5-8.5 scenario than the SSP2-4.5 scenario.The contribution rate of heavy precipitation (R95pTOT) increases by 22％ (41％) under SSP2-4.5 (SSP5-8.5) scenario.Climatological precipitation at the end of this century exhibits an obvious increasing trend across most regions of Southeast Asia under SSP2-4.5 and SSP5-8.5 scenarios,with large increases mainly observed in Kalimantan and New Guinea Island.Spatial patterns of precipitation alterations align closely with changes in P-E (precipitation minus evaporation),with the magnitude of evaporation change being modest.This suggests that the enhancement in climatological precipitation is the dominant factor contributing to the increase in P-E.Quantitative analysis of the moisture budget equation indicates that thermodynamic and dynamic effects mainly result in climatological precipitation changes by the end of the 21st century.The thermodynamic component,with higher inter-model consistency,contributes the most,accounting for 65％ (64％) of the P-E changes under SSP2-4.5 (SSP5-8.5) scenarios.However,dynamic effects counteract the changes in P-E,contributing 35％ (36％).Moisture convergence caused by atmospheric-specific humidity changes is considered the dominant factor in the projected precipitation increase.This conclusion underscores the need for a thorough assessment of the risks associated with extreme climatic conditions in Southeast Asia and emphasizes the importance of proactive measures to mitigate the risks posed by precipitation extremes in the region.

Abstract:In this study,utilizing the Normalized Difference Vegetation Index (NDVI) and meteorological data from 1982 to 2016 in the Qinghai region of China,we adopted a copula function method based on the Markov Chain Monte Carlo approach.Our goal was to explore the relationship between the NDVI and diurnal-nocturnal asymmetric warming during the growing seasons in the alpine climate zone of Qinghai Province from 1982 to 2016.The study results revealed the joint probability distribution and its seasonal differences between the diurnal-nocturnal warming and the NDVI.Initially,the paper presents the trends in seasonal diurnal-nocturnal temperatures and the NDVI over the years in the alpine climate region,followed by an analysis of diurnal-nocturnal temperatures using the copula function method to investigate the statistical characteristics between diurnal-nocturnal warming and the NDVI.It was found that the impact of diurnal-nocturnal warming on NDVI varies significantly from season to season,with the strongest correlation in autumn,followed by summer and spring.This suggests a clear seasonality in the influence of diurnal-nocturnal warming on vegetation growth.There is a specific temperature threshold between diurnal-nocturnal warming and the NDVI;when the diurnal-nocturnal warming is below this threshold,a positive correlation is observed,indicating that appropriate temperature increases promote vegetation growth.However,once this threshold is exceeded,the positive impact of diurnal-nocturnal warming on the NDVI becomes a suppression effect.Additionally,the paper examines the long-term impact of diurnal-nocturnal warming changes on vegetation growth by analyzing the joint probability distribution of diurnal-nocturnal warming and the NDVI during different recurrence periods.The results show that,in lower recurrence periods,the joint probability of diurnal-nocturnal warming with the NDVI is high,indicating more frequent favorable conditions for vegetation growth.Conversely,higher recurrence periods correspond to lower joint probabilities of diurnal-nocturnal warming with the NDVI,suggesting suppressed vegetation growth.This indicates that the relationship between diurnal-nocturnal warming and the NDVI becomes more complex within certain recurrence periods,which may be related to the regional climatic conditions,vegetation types,and other ecological factors.Overall,this study provides a new perspective on exploring the complex relationship between diurnal-nocturnal asymmetric warming and vegetation dynamics within the context of climate change.This is not only crucial for predicting and assessing the potential impact of future climate change on vegetation in alpine regions,but also provides a scientific basis for the management and conservation of these fragile ecosystems.

Abstract:South China has an important position and influence in economic,cultural and social development,and the SST anomalies in the Indian Ocean have a non-negligible impact on the weather and climate of South China.This paper explores the mechanism of precipitation anomalies during the second rainy season in South China to improve the theoretical system of precipitation anomalies in South China,and to provide a scientific basis for a deep understanding of drought and flooding anomalies,and work on prevention and mitigation of disasters in South China.In this paper,the relationship between IOD Modoki and precipitation anomalies during the second rainy season (July to September) in South China and possible mechanisms were investigated using CN05.1 Chinese regional high-resolution precipitation grid data for 1979—2019,SST data from Hadley Centre observations made in the UK,and ERA5 month-by-month atmospheric reanalysis data.The results show that the precipitation anomalies during the second rainy season in South China are significantly positively (negatively) correlated with the SST anomalies in the central (eastern and western) tropical Indian Ocean,manifesting as the spatial distribution of Indian Ocean IOD Modoki or Indian Ocean triple polariton events.After filtering out the influence of the ENSO signal,the precipitation anomalies during the second rainy season in South China are still more closely related to the IOD Modoki.The influences of positive IOD Modoki anomalies on the precipitation anomalies during the second rainy season in South China are as follows:1.The anomalous water vapour is transported westward from the eastern tropical Indian Ocean to the tropical Central Indian Ocean,then transported eastward to South China by the Koch force in the northern hemisphere,thus providing sufficient water vapour conditions in South China.The main water vapour convergence contributions to the positive precipitation anomaly in South China are the horizontal disturbance divergence term of the mean water vapour and the mean water vapour vertical advection term caused by the disturbance.2.Negative SST anomalies in the tropical southeast Indian Ocean cause southeasterly wind anomalies in the lower troposphere from the tropical southeast Indian Ocean to the tropical central Indian Ocean through the Mastuno—Gill response,enhancing the trans-equatorial flow near 70°E,which is then transported eastward to the northwest Pacific in the northern hemisphere,where it causes lower tropospheric cyclonic circulation anomalies.3.The anomalous low (high) tropospheric divergence (convergence) in the tropical East Indian Ocean and anomalous low (high) tropospheric convergence (divergence) anomalous convergence in South China enhance the local Hadley circulation in East Asia and favour precipitation generation in South China.4.The IOD Modoki causes the South Asian monsoon region to be controlled by anomalous downwelling motion,and causes positive vorticity anomalies around the eastern subtropical North Atlantic,North African desert region and western Mediterranean through the monsoon—desert mechanism.This in turn excites quasi-stationary Rossby waves propagating downstream along the rapids,and enhances the Japan Sea high pressure anomaly and lower tropospheric cyclonic circulation anomaly in South China and adjacent areas.All of these factors favour the production of precipitation in South China,while the absence of these factors is detrimental to the production of precipitation in South China.The above results are also verified in the numerical model.

Abstract:In the context of global warming,frequent extreme precipitation events pose a serious threat to people's livelihoods,production,and the economic development of society.The topography of North China (NC) is complex and diverse,and the distribution of precipitation is inconsistent due to the influence of terrain.In particular,there are significant geographical differences in the intensity and frequency of extreme precipitation.This study systematically analyzed the spatiotemporal distribution characteristics of precipitation amount,intensity,and frequency of extreme precipitation events in NC and its subregions.The analysis utilized daily precipitation data and station topographic height data from five provinces between May and September,spanning the period of 2012 to 2021.By calculating the 95th percentile of daily precipitation at each station throughout the study area,regional extreme precipitation events in NC were filtered.Geographically Weighted Regression (GWR) models were used to investigate the relationship between precipitation,the intensity and frequency of extreme precipitation events,and altitude.The results indicate that extreme precipitation in NC has exhibited multiple fluctuations over time,with significant regional variations.The plateau to the west of the Taihang Mountains and the plains to the east of the Taihang Mountains experienced more frequent yet fluctuating and low-intensity precipitation,while the plains to the south had less frequent yet gradually changing and significantly stronger precipitation.The spatial distribution of extreme precipitation indicates lower levels in the southern and northern regions and higher levels in the central region,influenced by factors such as topography.The extensive precipitation areas are located in the southeastern part of the Yanshan Mountains and at the junction of Shanxi,Hebei,and Henan Provinces.The average annual precipitation of the above two regions exceeds 200 mm,and the precipitation of some stations exceeds 280 mm.During extreme precipitation events,heavy rainfall often occurs at each weather station,indicating a tendency for clustering.The stations with the highest frequency of heavy precipitation are mainly located in the southeastern part of Shanxi Province.Moreover,stations that recorded maximum daily precipitation of over 300 mm are mainly concentrated in the transition zone between the Taihang and Yanshan Mountains,as well as the NC Plain.The diverse impact of regional topographies on precipitation intensity and frequency leads to significant variations in the characteristics of extreme precipitation,precipitation frequency,intensity,and daily maximum precipitation with altitude in the northern and southern parts of the NC area,which is divided by 38 °N.In the northern part of NC,the extreme precipitation,frequency,intensity,and daily maximum precipitation all decrease with the increase of altitude.There are significant regional differences in the variation of precipitation south of 38 °N with altitude,and the variables that dominate this difference vary from region to region.The significant increase in precipitation in the Linfen and Yuncheng areas of southern Shanxi with increasing altitude is due to the positive correlation between precipitation frequency and intensity with terrain.The main factor contributing to the decrease in precipitation near the Taihang Mountains with increasing altitude is the intensity of precipitation,not its frequency.

Abstract:During the dragon-boat precipitation period in 2022 (from May 21 to June 21),the average precipitation in South China was 472.5 mm,the second largest in history,the third largest in Guangdong and the largest in Guangxi since meteorological records began in 1951.This event led to river basin flooding in the Pearl River basin and catastrophic floods in the Beijiang River basin.Using daily precipitation data from 192 national meteorological observation stations in South China,NCEP/NCAR atmospheric circulation data,atmospheric outward long-wave radiation (OLR) data,and NOAA monthly-scale SST reanalysis data,we studied the characteristics of South China Dragon-boat precipitation anomaly in 2022 and its relationship with atmospheric circulation and sea surface temperature using correlation and composite analysis methods.The results indicate a significant strengthening of the East Asian trough and the northeast cold vortex,intensifying the cold air influence over South China.The southern branch trough also intensified,accompanied by an abnormal anticyclonic circulation east of the Philippines.The subtropical high strengthened,facilitating the transport of warm and humid air from the tropical Western Pacific and South Sea to South China via the southernly monsoon airflow.Additionally,the southwest monsoon warm and humid airflow persisted in South,supported by a strengthening blocking high pressure in the Okhotsk Sea.Ground sea-level pressure exhibited an abnormal distribution,with high pressure in the north and low pressure in the south across high to medium-low latitudes in the Northern Hemisphere.The significant upward movement in South China,coupled with evident water vapor convergence,resulted in extreme dragon-boat precipitation in South China in 2022.Furthermore,the La Niña event in the equatorial central eastern Pacific in 2022 significantly influenced atmospheric conditions,enhancing the Walker circulation.This suppressed convective activity east of the Philippines during the dragon-boat precipitation period,stimulating abnormal anticyclonic circulation and the appearance of Rossby waves in the atmosphere east of the Philippines.The abnormal southerly airflow from the South China Sea to South China facilitated the transport of warm and humid water vapor,contributing to the significant increase in dragon-boat precipitation in South China.

Abstract:The Three Rivers Source area is located in the southern part of Qinghai Province,China.It is the country's largest nature reserve,and the world's nature reserve with the highest concentration of biodiversity in a high-altitude area.Summer is when the most concentrated precipitation occurs there.Once rainstorm occurs,disasters such as landslides and flash floods very easily occur,which not only pose a serious threat to the safety of local people's lives and property,but also damage the local economic development.Improving the accuracy of numerical weather forecast in the Three Rivers Source area can improve the forecast level of rainstorm and effectively reduce losses due to disaster.Numerical weather forecasting involves initial boundary value problems,and the more accurate the initial field is,the more accurate the forecast results will be.The essence of data assimilation is to study how to process various unconventional observation data with different accuracies and integrate them reasonably with conventional observation data into an organic whole,so as to provide a more accurate initial field for numerical models and achieve the goal of improving the accuracy of model forecasting.Compared to other assimilation data,advantages of satellite data include consistent observation data,wide coverage,high spatiotemporal resolution,and being unaffected by geographical conditions.The Three Rivers Source area has high and complex terrain,vast area,and few meteorological observation stations,thus there are significant problems with the initial value quality in numerical models.However,satellite radiance data has the characteristics of wide coverage and high spatiotemporal resolution,thus it is expected to improve the current situation of insufficient conventional observation data in the Three Rivers Source area.For this reason,it is imperative to carry out research of MHS (microwave humidity sounder) data assimilation in the Three Rivers Source area,which lacks conventional data.The experimental research process was as follows:First,we selected three typical precipitation cases in the Three Rivers Source area:(June 30,2018,July 5,2018,and August 24,2018).Next,we used reanalysis data from NCEP (National Centers for Environmental Prediction) FNL (final operational global analysis),and added ATOVS (Advanced TIROS Operations Vertical Sounder) humidity detector MHS data assimilation.Then we conducted a cyclic assimilation test of three precipitation processes in the Three Rivers Source area based on the WRF (Weather Research and Forecasting) model and its three-dimensional variational assimilation system,to analyze the simulation status of the three events and perform quantitative analysis of the precipitation results.After the assimilation of the three precipitation events with the MHS data,the results show the following:(1) The amount of water vapor predicted increases,which is most obvious and highly consistent with the actual situation in the middle layer,and has good simulation of water vapor and wind,yet poor prediction of temperature at the ground level.(2) The impact of MHS data on precipitation forecasts is mainly reflected in the expansion of precipitation areas and the increase of precipitation forecasts,but it increases the number of empty reporting areas.(3) From the results of the TS (threat score),ETS (equitable threat score),and POD (probability of detection) score,two precipitation tests showed significant improvement.Among them,the TS score of “0630” test improved by 0.05—0.1 for 0.5—10 mm,ETS improved by more than 0.08 for 5 mm,and also improved by a small amount of approximately 10—20 mm,and the POD test improved between 0.5—20 mm.The most obvious improvement was 0.1—0.25 in the range of 0.5—10 mm,while the TS and ETS of the “0824” test improved by more than 0.1 in the range of 10—20 mm,and the POD test improved by 0.1—0.4 in the range of 6—20 mm.The assimilation improved the precipitation forecast,which was particularly evident in the large-threshold precipitation.(4) The assimilation of MHS data did not significantly improve the precipitation forecast of the “0705” experiment,which indicates that assimilation does not always yield positive effects in the results.Therefore,one should not overly trust it when using MHS data.In general,assimilating MHS data can improve the forecast quality.In addition,there remain the following shortcomings in this experiment:The selected assimilation data is not highly varied,and the three selected precipitation cases are all strong precipitation in shortwave troughs,which may not be applicable to precipitation in other weather systems.Therefore,in the future,higher precision satellite data and microwave humidity data from other satellites can be considered to conduct assimilation experiments on various types of precipitation,so as to further verify the assimilation effect of satellite data in the assimilation prediction system of the Three River Source area.

Abstract:As the most fundamental physical quantity for research 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,the probability of observation interruptions or data quality anomalies caused by factors such as instrument failures,communication interruptions and natural disasters has greatly increased.Missing data,characterized by random distribution of stations and random lengths of series,may cause significant obstacles to climate analysis and operational applications.At present,there are two main technical solutions for interpolation of missing daily temperature data.One is the climate correlation statistics solution based on historical data,which selects the optimal reference sequence to achieve data substitution.This solution has high interpolation accuracy,yet low timeliness.The second solution is the spatial interpolation scheme,which can achieve real-time interpolation,but the interpolation effect heavily depends on the density level of station distribution,and the interpolation stability is poor.To address the shortcomings of existing meteorological data interpolation solutions,this paper proposes a new real-time interpolation method for missing daily temperature data based on Dynamic Time Warping (DTW).The method adopts a twice interpolation strategy,and the main technical content includes the following:(1) The core technical route of the method is to decompose the temperature series into fitting straight lines and residual curves by using a univariate linear regression equation,then the two are reconstructed to achieve the reorganization of the temperature series.(2) The method provides the concept and interpolation conditions for defining the temperature interpolation area,as follows:The continuous missing data area is referred to as the interpolation area (Zone B).If the length of the left neighbor sequence (Zone A) and right neighbor sequence (Zone C) is consistent with the interpolation area (Zone B),and the data in Zones A and C are also complete,then it will be confirmed that the missing data in the interpolation area (Zone B) can be interpolated.(3) The method proposes using dynamic time warping to calculate the DTW distance between two time series,to determine the optimal reference station for interpolation in real time.(4) The method demonstrates the two interpolation processes,where the primary interpolation directly replaces the interpolation area (Zone B) with reference station data,and the twice interpolation reconstructs the fitted straight line and residual curve derived from the separation of the primary interpolation through cross combination.This paper uses the collected temperature data of Shandong Province for 2021 to conduct a double random test on the method.The inspection method adopts the method of leaving the observed true value blank to simulate the missing data in the interpolation area (Zone B),and calculates the error between the interpolation value and observed true value after the interpolation.The inspection process implements the conditional combination coverage method,recording all interpolation results that can be generated by the combination of interpolation stages (primary and twice interpolation) and distance measurements (such as DTW distance and geographic distance,including horizontal and altitude distance).The test results show that the interpolation method proposed in this paper can meet the interpolation needs of daily mean temperature,daily maximum temperature,and daily minimum temperature data.The combination of DTW distance measurement and twice interpolation can achieve a better effect than the commonly used combination methods based on site geographical proximity relationships.The method has a certain sensitivity to terrain,and its interpolation effect is better in plain or hilly areas than in mountainous ones.The twice interpolation mechanism proposed in this paper has broad application prospects for solving the problem of missing meteorological data with double random characteristics,and can also provide a good reference for the homogenization correction of historical long series meteorological data.

Abstract:This study aims to accumulate prediction experience of local rainstorm process,enhance understanding of heavy rainfall water vapor characteristics,and improve rainstorm prediction ability.Analyzing precipitation data from automatic meteorological observation stations,the China precipitation dataset,ERA5 reanalysis data,and NCEP/NCAR reanalysis data,we investigate the water vapor transport characteristics of a severe waterlogging rainstorm process in Wuhu City during the early morning of June 5,2022.Utilizing methods such as water vapor budget analysis,HYSPLIT backward trajectory tracking,and water vapor transport contribution rate analysis,our findings reveal that the rainstorm occurred in the 200 hPa diverging area and the left front of the 850 hPa low-level jet stream.The continuous impacts of the 500 hPa cold air on the low-level jet stream triggered convective cloud clusters,resulting in strong precipitation.The high-level divergence and low-level convergence enhanced horizontal convergence and vertical transport of water vapor,while the southwest low-level jet stream strengthened and transported water vapor to the rainstorm area,providing the necessary conditions for the occurrence of the rainstorm.Consequently,Wuhu City had abundant water vapor before the heavy rainfall event,with a deep and continuously humidified wet layer.The total column water increased by 4.1 kg·m^-2 in 6 hours,with the rainstorm occurring at the highest value of continuous water vapor increase.During the rainstorm,key parameters such as total column water,specific humidity of 850 hPa,water vapor flux,and water vapor flux divergence reached significant levels,correlating well with the intensity of rainstorm.Our analysis attributes these conditions to the strengthening southwest low-level jet stream from the Bay of Bengal to the lower reaches of the Yangtze River,continuously transporting water vapor to Wuhu City.After the rainstorm,water vapor quantities decreased significantly.The results of the water vapor budget and water vapor tracking analysis showed that before the rainstorm occurred,water vapor inflow mainly occurred at the western and southern boundaries of the lower troposphere.The inflow layer was deep,and there was upward vertical transport of water vapor.The quantity of water vapor inflow into the entire layer was approximately 56.0×10⁷ t·h^-1,with a main inflow height of 850—700 hPa.The maximum quantity of water vapor inflow into a single layer could reach 9.0×10⁷ t·h^-1,originating mainly from 1 000 meters above the Bay of Bengal and the South China Sea,with their water vapor channel trajectory accounting for 32.0％ and their water vapor transport contribution rate of 55.4％.The other two sources of water vapor in the northwest passage were 7 000 meters above the Baltic Sea and 3 000 meters below the Ural River.When the rainstorm occurred,the main inflow layer decreased to 850 hPa,and the southern boundary turned to outflow at 700 hPa.The inflow of the entire layer decreased to about 21.0×10⁷ t·h^-1,weakening vertical water vapor transport,with the total net inflow height of water vapor concentrated at 850 hPa and a net inflow quantity of about 1.0×10⁷ t·h^-1.The water vapor mainly originated from 1 000 meters above the South China Sea,with their water vapor channel trajectory accounting for 46.0％ and their water vapor transport contribution rate of 60.3％.The other two sources of water vapor in the northwest passage were over 2 000 meters east of the lower reaches of the Ural River and over 7 000 meters above the Norwegian Sea.The Bay of Bengal and the South China Sea were the main sources of water vapor during the rainstorm.The study underscores that while water vapor conditions are essential for rainstorm formation,other factors such as trigger mechanisms and impact system locations are crucial in understanding waterlogging events.Future research should focus on integrating multi-source detection data to enhance rainstorm prediction and early warning capabilities.

Abstract:With the advancement of China's aviation industry,particularly through the continuous breakthroughs in the development of domestic civilian aircraft models,meteorological services and technical research for aircraft flight tests have garnered increasing attention from various sectors.Due to the unique,high-risk,and complex nature of flight test scenarios,new demands have been placed on meteorological services.Compared to typical civil aviation meteorological service settings,flight tests require more stringent weather conditions,have different thresholds for high impact weather events,and unlike commercial aviation,which avoids hazardous weather,flight tests need to seek out specific weather conditions.Consequently,there is a higher demand for precise forecasts of high impact weather,particularly with a lower tolerance for false alarms.Flight test meteorological services encompass seamless forecasting and prediction,climate condition analysis,observational plan design for flight test campaign,and interpretation of meteorological standards for airworthiness,etc.This article analyzes the current state of meteorological services for flight tests in China,focusing on milestone events during type certification flight tests and key subjects such as aircraft icing.It is found that China possesses relatively abundant flight test climatic resources,which is sufficient to guarantee the relevant aircraft models to complete all the special weather flight tests in domestic environment for type certification.Utilizing long-term,multi-source meteorological observation data and effective diagnostic algorithms can enhance flight test scheduling more rational and more scientific,thereby improving flight test efficiency.More accurate aircraft icing potential forecasting methods,combined with analyses of aircraft icing meteorological characteristics based on multi-source observations,have enabled efficient organization and accomplishment of domestic aircraft type icing flight tests,also providing preliminary insights into the microphysical characteristics of super-cooled clouds in China where aircraft icing occurs.However,the phenomenon of “over-prediction” in aircraft turbulence index forecasting algorithms still presents challenges in accurately capturing turbulence during flight tests.Considering future needs with further advancements in aircraft manufacturing and flight test in China,in-depth research in meteorological support technologies for flight test,like aircraft icing and turbulence,remains necessary,such as studying different icing environments and their transformation mechanisms and integrating artificial intelligence with physical methods to improve turbulence forecasting accuracy.

Abstract:Wire icing is a disastrous phenomenon that can harm the operation of the power system,and has been identified a meteorological disaster focused on by the government,electric power,and meteorological departments.Accurately predicting conductor icing is crucial for ensuring the safe operation of transmission lines and reducing disaster losses caused by wire icing.In mountainous environments,the prediction accuracy of power line ice accumulation depends on the downscaling capability of numerical models for meteorological fields,but the research on coupled dynamical downscaling methods for icing prediction is insufficient.In this study,by using the CALMET downscaling model coupled with the WRF model,the meteorological field of the icing event in the Zhongtiao Mountain of southern Shanxi Province during the period of 5—10 January,2022 was simulated and evaluated.On this basis,these meteorological fields were used to drive the Makkonen model to simulate the conductor icing process under different topographies.The simulation results of the WRF and CALMET models were examined separately,and the conclusions are as follows:1) Compared with the WRF model,the meteorological field downscaled by the CALMET model can more appropriately represent the distribution of the near-surface temperature and wind fields under the complex topography.The low-level wind field simulated by the CALMET model during the icing period is more consistent with the distribution pattern of slope surface flow and terrain bypass flow in actual topography.Meanwhile,the simulated cold region (air temperature <0 ℃) has a larger extent than that of the WRF simulation,which is more consistent with the observation.2) The air temperature and wind field simulated by the CALMET model show high agreement with the observation.The root mean square error (RMSE) of air temperature in CALMET is reduced by 0.5—1 ℃ and the correlation coefficient is improved from 0.5—0.8 to 0.6—0.85 compared with the WRF model.In addition,the RMSE of wind speed in CALMET is reduced by 1 m/s and the correlation coefficient is improved by 0.2 compared with the WRF model.This also indicates that the meteorological field simulated by the WRF model combined with CALMET is closer to the icing environment.3) The Makkonen method coupled with the CALMET model is able to reasonably reproduce the spatial and temporal distribution of icing process under different topographies.The error of the simulated ice thickness at each tower is significantly reduced by 2 mm compared with that of the WRF model,and the lag time of the simulated ice initiation is significantly reduced.In addition,the spatial distribustion of several high icing thickness areas simulated by the CALMET model fits better with the distribution of terrain elevation,particularly at the higher elevation of the south slope of Zhongtiao Mountain,where the simulated ice thickness ranges from 3.2 to 6.4 mm,which is consistent with the observation.In summary,the results of this study will help to optimize the simulation effects of meteorological factors and wire icing events in mountainous areas,which in turn will provide further assistance to exploring the deeper influence mechanisms of different microtopographies on conductor icing.