• Current Issue
  • Online First
  • Archive
  • Most Downloaded
  • Special Issue
  • Special Column
    Select AllDeselectExport13
    Display Method:
    2022,45(6):801-814, DOI: 10.13878/j.cnki.dqkxxb.20220507005
    Abstract:
    The terrestrial ecosystem can absorb about 30% of the anthropogenic carbon emissions through vegetation photosynthesis,which plays an important role in the global carbon cycle and slowing the rise of atmospheric carbon dioxide concentration.The solar-induced chlorophyll fluorescence (SIF) remote sensing technology developed in the last 10 years can monitor the actual photosynthesis of vegetation,providing new ideas and methods for the study of global terrestrial ecosystem carbon cycle.This paper reviews the recent progresses in the study of satellite-based SIF observations and their applications in terrestrial ecosystem carbon cycling and land-atmosphere interaction,especially in the estimation of global vegetation gross primary productivity and the development of terrestrial ecosystem carbon cycle models,and further discusses the challenges faced by research in this field and the future development directions.
    2022,45(6):815-825, 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,as well as the limitations of the ensemble forecast system in terms of initial perturbation schemes,ensemble size,etc.,the forecast results are generally biased.In addition,different forecasting models usually have different physical parameterization schemes,initial conditions,etc.,resulting in different forecasting capabilities.Therefore,how to correct the forecast deviation 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,and has become a statistical post-processing method to effectively eliminate forecast deviation and improve weather forecasting skills.For the three most basic surface meteorological variables (i.e.,temperature,precipitation and 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,this paper discusses the issues that need to be paid attention to when using and developing multimodel ensemble technologies,including considering the number of participating models,developing precipitation and wind speed classification forecast models,and developing new multimodel ensemble technologies based on machine learning.
    2022,45(6):826-834, DOI: 10.13878/j.cnki.dqkxxb.20220928001
    Abstract:
    Based on the content of Chapter 7 from the Sixth Assessment Report (AR6) contributed by the Intergovernmental Panel on Climate Change (IPCC) Working Group I (WGI),this paper interprets the dependence of climate feedbacks on temperature patterns in detail.Compared with the Fifth Assessment Report (AR5),the understanding of the relationship between transient changes in climate feedback and evolving spatial patterns of surface temperature has been greatly improved in AR6.The assessments show that under greenhouse gas forcing,it is very likely that the warming in the Arctic will be more pronounced than on global average over the 21st century.On centennial timescales,the Antarctic will warm more than the tropics and the tropical Pacific Ocean east-west sea surface temperature (SST) gradient will weaken,with greater warming in the east than the west.AR6 identifies changes in the degree of polar amplification over time,particularly in the Southern Hemisphere,and changes in the tropical Pacific Ocean east-west SST gradient over time as key factors affecting how climate feedbacks may evolve in the future.Climate feedbacks,particularly from clouds,are expected to become less negative (more amplifying) on multi-decadal timescales as the spatial pattern of surface warming evolves.
    2022,45(6):835-849, DOI: 10.13878/j.cnki.dqkxxb.20211101001
    Abstract:
    The Yangtze River Basin(YRB) is one of the areas with a high frequency of heatwave occurrences in China.The daily maximum temperature (Tmax) in this area shows significant low-frequency oscillation signals for (10—30 d and 30—60 d) time periods.Based on the results of the lead-lag correlation analysis between the YRB Tmax and the 10—30 d/30—60 d convection and circulation anomalies,we identify the main low-frequency signals affecting the YRB Tmax.There are three types of signals that travel in different directions:1) the eastward and southward signals from the Eurasian continent;2) circulation anomalies propagating southwestward from Northeast Asia;and 3) low-frequency convective signals propagating from the western Pacific toward East Asia.The temperature diagnostic equation results show that when the low-frequency convection/circulation anomalies approach the YRB,both the diabatic (clear-sky radiative heating) and adiabatic (associated with sinking motion) heating processes lead to variations in the YRB temperature.To identify these precursory signals objectively and efficiently,as well as consider the nonlinear interaction between YRB Tmax and the large-scale predictors,we use Convolutional Neural Network (CNN),a type of deep neural network,to train the historical data,and then develop an extended-range forecast model for YRB Tmax.The independent forecast results show that the CNN-based forecast model is capable of predicting the YRB Tmax at a 30-day lead time,with the temporal correlation coefficient between the forecast and observed Tmax of 0.63—0.70 (exceeding the 99% confidence level).The current results suggest the potential of CNN in the application of extended-range forecasting as the magnitude of error (root-mean-square error) is less than one standard deviation.
    2022,45(6):850-862, DOI: 10.13878/j.cnki.dqkxxb.20220615001
    Abstract:
    Precise weather monitoring and accurate weather forecast are two of the most decisive factors for the success of the Winter Olympics.Considering the particularity of the 2022 Beijing Winter Olympic Games (the only Winter Olympic Games held under the climate dominated by the continental East Asian winter monsoon and the only Winter Olympic Games held in inland areas) and the rigid demand for the goal of “hundred-meter resolution and minute-updated level” high-precision forecast,the Beijing Institute of Urban Meteorology has developed a new generation of the Rapid-refresh Integrated Seamless Ensemble system—RISE—that can provide 500-and even 100-m resolution spatial grid forecast data products with 10-min updated frequency for the Beijing Winter Olympics.In order to improve the prediction accuracy of the RISE system,and considering the successful use of deep learning in the field of geoscience in recent years,this paper develops a convolution neural network-based model,Rise-Unet,using the high-resolution RISE data from 2019 to 2021 to correct the prediction results of 2-m surface temperature,2 m-relative humidity,10-m U wind speed,and 10-m V wind speed for a lead time of 4—12 hours.The root-mean-square error and mean absolute error are employed to evaluate the accuracy of the model in this study.By comparing with the original prediction results of the RISE system,it is proven that the deep learning-based model,Rise-Unet,can effectively improve the accuracy of high-resolution gridded prediction results.The method proposed in this study can be applied as the post-processing module of the RISE system,which has important scientific significance and application value for improving the grided prediction level of the RISE system as well as other high-resolution numerical weather forecasting systems.
    2022,45(6):863-878, DOI: 10.13878/j.cnki.dqkxxb.20220317007
    Abstract:
    Based on the observations of daily snowfall,precipitation,temperature,relative humidity,air pressure and wind speed in China,a distinguishing method for the snowfall events based on the Logistic regression approach is constructed,and the applicability of this method and other widely used snowfall distinguishing methods is compared.Results show that the single temperature threshold and S-curve methods are relatively uncertain for snowfall simulation within the temperature range from -3 ℃ to 4 ℃.By contrast,the series of Logistic fitting methods have higher success rates in determining snowfall events,and are more robust for snowfall recognition in different regions of China,especially in the Tibetan Plateau.In the Logistic methods,temperature and relative humidity play a decisive role in determining snowfall,while the influences of air pressure and wind speed are relatively small.The Logistic wet-bulb temperature scheme (LogTw) and the air temperature+relative humidity scheme (LogTaHR) can well reproduce the spatial distribution and interannual variation characteristics of snowfall,and the corresponding deviationsare smaller than other methods.On the whole,there is little difference between the two schemes for snowfall recognition.Therefore,the LogTw or LogTaHR scheme can be used to identify snowfall events in China,especially the snowfall events in climate models.
    2022,45(6):879-889, DOI: 10.13878/j.cnki.dqkxxb.20211228001
    Abstract:
    As one of the storage methods of water resources on the earth,snow cover has an important impact on soil moisture and freshwater distribution.The north-south crossing latitude in China is about 50°,and snow cover is widely distributed.It is of great significance to study the spatial and temporal dynamics of snow cover.Based on the snow cover data of FY-3 meteorological satellite,this paper proposesa trend extraction method based on empirical mode decomposition,and discusses the spatiotemporal dynamic change trend of snow cover in China from 2010 to 2019.Results show that:1) Snow cover frequency (SCF) in China has significant seasonal characteristics,which increases first and then decreases.The SCF reaches the maximum in February and March every year.The SCF in Northeast China shows an interannual trend of significant decline,while that in other regions does not change much.2)The overall snow cover rate (SCR) in China (NeiMonggol and Tibet) decreases by about 1.2% (about 1.5%),while that in other regions does not change significantly.The SCR in main snow cover areas changes from increasing to decreasing in 2016.
    2022,45(6):890-903, DOI: 10.13878/j.cnki.dqkxxb.20211112001
    Abstract:
    Using the output data of 47 CMIP6 models and the NCEP/NCAR reanalysis data from 1958 to 2014,this paper studies seasonal variation characteristics of interhemispheric oscillation (IHO) in the model atmosphere,and evaluates the ability of CMIP6 to simulate the seasonal characteristics of IHO.Results show that all 47 CMIP6 models can simulate the seasonal evolution characteristics of IHO,but there are some differences among the models.Through comparison,16 better models for simulating the seasonal cycle of IHO are selected,which can successfully simulate the temporal evolution and spatial structure of hemispheric atmospheric mass.Further analysis shows that water vapor can counteract the seasonal variation of IHO,and the change of water vapor mass in the hemispheres can drive the generation of cross-equatorial mass flow.The surface net short wave radiation is higher in summer and lower in winter,and the water vapor evaporation caused by its heating plays an important role in the change of water vapor mass.The surface net long wave radiation changes greatly in spring and autumn,which is consistent with the monthly change of atmospheric mass.Compared with the reanalysis data,it shows that the peak valley variations of hemispheric atmospheric mass simulated by CMIP6 models have obvious monthly deviations,and the deviations of surface pressure anomalies simulated by CMIP6 models mainly occur in the North Pacific,Eurasia,mid-latitude regions of the Southern Hemisphere and polar areas.There are obvious deviations in the simulated evaporation and precipitation,equatorial wind field,surface net long wave and short wave radiation fluxes in the Northern and Southern Hemispheres.
    2022,45(6):904-916, DOI: 10.13878/j.cnki.dqkxxb.20210113002
    Abstract:
    In this paper,the environmental meteorological stations in Xi'an are classified into urban,suburban,and two types of rural stations based on the proportion of construction land and the information entropy of land use.Following that,the urban-rural distribution characteristics of PM2.5 are then explored,as well as the correlation between the urban-rural PM2.5 concentration difference and the urban heat island effect intensity (UHII).Results show that the urban-rural distribution characteristics and the diurnal variation characteristics of PM2.5 in Xi'an vary with the season.The PM2.5 concentrations of the two types of rural stations are significantly different.Evidently,the correlation coefficients between UHIID and PM2.5 concentration of urban (rural D) stations are higher than those between UHIIU and PM2.5 concentration of urban (rural U) stations.Furthermore,because of more emissions,the correlation coefficients between UHIIU (UHIID) and PM2.5 concentration of rural stations are greater than those between UHIIU (UHIID) and PM2.5 concentration of urban stations.UHIID-RUPIID demonstrates a negative correlation in the spring,summer and autumn as UHIID increases,with the relative urban-rural PM2.5 concentration difference (RUPIID) showing a downward trend.In addition,the urban area's vertical diffusion capacity of PM2.5 is greater than its horizontal diffusion capacity the larger the UHIID.By changing the transmission and diffusion characteristics of PM2.5,UHIID further influences RUPIID in Xi'an.
    2022,45(6):917-925, DOI: 10.13878/j.cnki.dqkxxb.20191128012
    Abstract:
    In this paper,a southwest vortex rainfall process of 8—9 July 2010 over Sichuan Basin is simulated using the Mesoscale Weather Research and Forecasting (WRF) model.The simulation results are then utilized to estimate the entrainment rate in convective clouds under 0 ℃ layer.It is found that the vertical distribution characteristics of simulated precipitation particles are roughly consistent with the observed characteristics,proving that the WRF accurately simulates the vertical distribution of precipitation particles in the southwest vortex precipitation cloud system.The simulation results show that the liquid water content,vertical velocity and buoyancy in the clouds increase with height above the cloud base and decrease with height close to the cloud top.However,the moisture static energy (MSE) mainly reduces with cloud height.In the case of entrained air far away from the cloud edges,the entrainment rate decreases with height above the cloud base and increases with height towards the cloud top,while the intensity of the entrainment rate towards the cloud top weakens,and the mean entrainment rate decreases.In addition,the entrainment rate is negatively correlated with both cloud water content and rain water content in cloud,indicating that the entrainment increases the evaporation of cloud droplets,reduces the size of droplets and consequently suppress the development of convective clouds and precipitation.When we assume that the entrained environmental air surrounds the cloud edge,the estimated entrainment rate is higher than the corresponding value when we assume that the entrained air is far away from the cloud edge.However,the vertical distribution characteristics of entrainment rate and the influence of entrained air on the clouds are similar under the two assumptions.Both assumptions have advantages and disadvantages that should be considered when determining the entrapment rate of the southwest vortex cloud system.Moreover,the entrainment rate decreases over time,which is related to the development and growth of the cloud ensemble during this time.The results in this paper provide a theoretical basis for further research on entrainment rate parameterization in the Sichuan basin.
    2022,45(6):926-937, DOI: 10.13878/j.cnki.dqkxxb.20211121001
    Abstract:
    At present,a Regional Ensemble Prediction System has been developed by the Kelamayi Meteorological Bureau.The system has only adopted the initial condition perturbation of the Ensemble Transform Kalman Filter (ETKF),which causes the system lack spread.In order to improve the performance of this Ensemble Prediction System,the model perturbation method of Stochastic Physics Parameterization Tendency (SPPT) is adopted and tested.Firstly,SPPT scientific parameters for the system are determined and set using a sensitivity test on the critical parameters of SPPT.Secondly,an ensemble forecast experiment test based on the Kelamayi Ensemble Prediction System is conducted to compare the ETKF initial perturbation scheme with the combination of the ETKF initial perturbation and SPPT model perturbation (ETKF-SPPT) scheme.The results show that the ETKF method provides initial condition perturbations with dynamic structure,while the spread saturates within a short forecast lead time and decreases due to the constraint of identical Lateral Boundary Condition (LBC) for all members.Adopting the SPPT model perturbation method significantly improves the ensemble spread for all forecast lead times.Based on the ensemble verification,adding model perturbation to initial perturbation condition improves the probabilistic forecast skill with a higher forecast reliability and a smaller RMSE.Without model perturbation,ETKF’s reliability is low and its root mean square error (RMSE) is relatively large.Additionally investigated and examined is a local gale case that occurred in Kelamayi during the experimental period.Its results show that employing model perturbation significantly improves the ensemble forecast,resulting in a more accurate forecast of the local gale’s magnitude and duration.
    Select AllDeselectExport
    Display Method:

    Select AllDeselectExport
    Display Method:
    2014,37(5):642-652, DOI: 10.13878/j.cnki.dqkxxb.20121017006
    [Abstract] (2486) [HTML] (0) [PDF 12.46 M] (15175)
    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.
    2011(1):14-27, DOI:
    [Abstract] (2888) [HTML] (0) [PDF 15.30 M] (14182)
    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.
    2019,42(4):631-640, DOI: 10.13878/j.cnki.dqkxxb.20170815015
    [Abstract] (704) [HTML] (0) [PDF 6.93 M] (13972)
    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.
    2014,37(2):129-137, DOI:
    [Abstract] (2196) [HTML] (0) [PDF 13.30 M] (12693)
    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.
    2021,44(1):39-49, DOI: 10.13878/j.cnki.dqkxxb.20201113007
    [Abstract] (248) [HTML] (233) [PDF 37.05 M] (9274)
    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.
    2015,38(1):27-36, DOI: 10.13878/j.cnki.dqkxxb.20130626001
    [Abstract] (1993) [HTML] (0) [PDF 20.93 M] (8728)
    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.
    2022,45(4):502-511, DOI: 10.13878/j.cnki.dqkxxb.20220529013
    [Abstract] (141) [HTML] (79) [PDF 29.68 M] (7262)
    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.
    2014,37(5):653-664, DOI: 10.13878/j.cnki.dqkxxb.20111230001
    [Abstract] (2138) [HTML] (0) [PDF 33.55 M] (6803)
    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.
    2010(6):667-679, DOI:
    [Abstract] (2382) [HTML] (0) [PDF 2.74 M] (6564)
    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。
    2011(2):251-256, DOI:
    [Abstract] (2279) [HTML] (0) [PDF 2.67 M] (6431)
    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.
    2015,38(2):184-194, DOI: 10.13878/j.cnki.dqkxxb.20140508002
    [Abstract] (1943) [HTML] (0) [PDF 16.29 M] (6063)
    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.
    2010(6):738-744, DOI:
    [Abstract] (2264) [HTML] (0) [PDF 2.05 M] (5806)
    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
    2013(1):37-46, DOI:
    [Abstract] (3382) [HTML] (0) [PDF 4.97 M] (5697)
    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.
    2010(5):593-599, DOI:
    [Abstract] (2153) [HTML] (0) [PDF 1.04 M] (5658)
    Abstract:
    采用中国1951-2006年的气象观测资料,在运用度日分析法分析全国、各省及省会城市近50a的平均温度变化的基础上,系统分析全国的度日变化情况;研究了中国六大区省会城市热度日(Heating Degree day,HDD)和冷度日(Cooling Degree day,CDD)的变化情况;分析了典型城北京、上海、广州CDD的上升趋势和上升率,并求得相关方程。结果表明,北京、上海、广州CDD的长期变化都呈上升趋势,上升率分别为117℃/(10a)、104℃/(10a)、251℃/(10a)。
    2010(6):697-702, DOI:
    [Abstract] (2191) [HTML] (0) [PDF 1.61 M] (5595)
    Abstract:
    利用2009年春季内蒙古苏尼特左旗风速、空气温湿度的野外观测资料,用梯度法研究荒漠化草原区空气动力学粗糙度Z0时发现,Z0有明显的日、月分布规律。中性条件下,根据风速对Z0的不同影响可分为3个特征区。梯度法计算Z0有风速条件约束,只有在风速较大时计算的Z0真实可靠,确定可靠风速区域是正确应用梯度法计算Z0的关键。Z0随风速值的增大成指数关系递减,可从指数函数的收敛性确定Z0。
    2010(4):489-495, DOI:
    [Abstract] (2532) [HTML] (0) [PDF 1.91 M] (5489)
    Abstract:
    利用TRMM卫星上携带的闪电探测仪(LIS)所获取的10a闪电资料(1998—2007年)对西南地区闪电活动的时空分布特征进行了分析。结果表明:该地区闪电次数的年差异较大,最多年份是最少年份的2倍多,闪电活动季节性特征非常明显,闪电主要集中在春末仲夏发生,呈现单峰值特征,4—8月是闪电高发期(约占全年总闪电活动的8483%)。闪电活动的日变化表明,闪电峰值区集中在傍晚、午夜前后两个时段,闪电谷值区出现在09:00—12:00,夜雷暴多,这是与其他地区闪电日变化显著不同的地方。在对闪电次数进行了探测效率订正后,根据LIS注视时间,计算了闪电密度。西南地区闪电密度分布大体呈现:东部高,西部低;南部高,北部低。闪电密度较高、面积较大的高值中心位于中越交界的老山一带,非常明显的大片低发区主要位于西南西部地区。研究表明:西南地区闪电时空分布与当地的地形地势、水汽和地理环境条件等诸多因素有关。
    2010(4):385-394, DOI:
    [Abstract] (2303) [HTML] (0) [PDF 1.49 M] (5456)
    Abstract:
    利用南方地区多个气象站和电力部门观冰站的导线覆冰逐日冰厚资料,将广义极值分布和广义帕雷托分布引入导线覆冰的概率模型研究中,通过超门限覆冰次数的泊松分布拟合检验,结合Hill图解,提出了基于超门限峰值法门限值的确定方法;对两种分布在导线覆冰极值模型拟合的适用性研究表明,广义帕雷托分布对各站覆冰冰厚极值的拟合精度最高;重现期冰厚极值估计随样本长度的变化分析表明,广义帕雷托分布模型极值估计的稳定性比广义极值分布强,一般样本容量达到25a左右时,广义帕雷托分布重现期冰厚极值的估计趋于稳定,可以作为短序列下估计导线覆冰极值的较好方法。

Search

  • Search by:
  • Search term:
  • from to

External Links

Address:

Postcode:

Phone: