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Efficiently Improving Ensemble Forecasts of Warm-Sector Heavy Rainfall over Coastal Southern China: Targeted Assimilation to Reduce the Critical Initial Field Errors

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气象学报:英文版 2023年第4期37卷 486-507页

作者： Xinghua BAO Rudi XIA Yali LUO Jian YUE State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences(CAMS)China Meteorological Administration(CMA)Beijing 100081 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science&TechnologyNanjing 210044 cma earth system modeling and prediction centre China Meteorological AdministrationBeijing 100081

Warm-sector heavy rainfall events over southern China are difficult to accurately forecast, due in part to inaccurate initial fields in numerical weather prediction models. In order to determine an efficient way of reducing the critical initial field errors, this study conducts and compares two sets of 60-member ensemble forecast experiments of a warm-sector heavy rainfall event over coastal southern China without data assimilation(NODA) and with radar radial velocity data assimilation(RadarDA). Yangjiang radar data, which can provide offshore high-resolution wind field information, were assimilated by using a Weather Research and Forecasting(WRF)-based ensemble Kalman filter(EnKF) system. The results show that the speed and direction errors of the southeasterly airflow in the marine boundary layer over the northern South China Sea may primarily be responsible for the forecast errors in rainfall and convection evolution. Targeted assimilation of radial velocity data from the Yangjiang radar can reduce the critical initial field errors of most members, resulting in improvements to the ensemble forecast. Specifically, RadarDA simulations indicate that radial-velocity data assimilation(VrDA) can directly reduce the initial field errors in wind speed and direction, and indirectly and slightly adjust the initial moisture fields in most members, thereby improving the evolution features of moisture transport during the subsequent forecast period. Therefore, these RadarDA members can better capture the initiation and development of convection and have higher forecast skill for the convection evolution and rainfall. The improvement in the deterministic forecasts of most members results in an improved overall ensemble forecast performance. However, VrDA sometimes results in inappropriate adjustment of the initial wind field,so the forecast skill of a few members decreases rather than increases after VrDA. This suggests that a degree of uncertainty remains about the effect of

关键词： ensemble forecast targeted assimilation warm-sector heavy rainfall

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Random Forest-Based Snow Cover Mapping in China Using Fengyun-3B VIRR Data

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Journal of Meteorological Research 2023年第5期37卷 666-689页

作者： Yuchen XIE Yonghong LIU Yeping ZHANG Fuzhong WENG Shanyou ZHU Zhaojun ZHENG Shihao TANG School of Remote Sensing&Geomatics Engineering Nanjing University of Information Science&TechnologyNanjing 210044 cma earth system modeling and prediction centre China Meteorological Administration(CMA)Beijing 100081 State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081 National Satellite Meteorological centre China Meteorological AdministrationBeijing 100081

Currently,there is variability in the spectral band thresholds for snow cover recognition using remote sensing in different regions and for complex *** Fengyun-3B Visible and Infra-Red Radiometer(FY-3B VIRR)satellite data,we applied random forest(RF)methodology and selected 13 feature variables to obtain snow cover.A training set was generated,containing approximately 1 million snow and nonsnow samples obtained in China from the snow monitoring reports issued by the National Satellite Meteorological centre and four snow cover products from the Interactive Multi-sensor Snow and Ice Mapping system(IMS),the FY-3B Multi-Sensor Synergy(MULSS),the Moderate Resolution Imaging Spectroradiometer(MODIS)snow cover product(MYD10A1),and the National Cryosphere Desert Data Center(NCDC).This training set contained many different samples of cloud types and snow under forest cover to help effectively distinguish snow and clouds and improve the recognition rate of snow under forest ***,two RF snow cover recognition models were constructed for the snow and nonsnow seasons and they were used to conduct daily snow cover recognition in China from 2011 to *** results show that the RF models constructed based on FY-3B VIRR data have good recognition performance for shallow snow,understory snow,and snow on the Qinghai–Tibetan *** recognition accuracy against weather stations and the spatial consistency with the IMS product are better than the MULSS,MYD10A1,and NCDC *** overall accuracy of the RF product is 90.6%,and the recall rate is 93.8%.The omission and commission errors are 6.2%and11.1%,*** other existing snow cover algorithms,the established RF model skips the complicated atmospheric correction and cloud identification processes and does not involve external auxiliary data;thus,it is more easily popularized and operationally applicable to generating long-time series snow cover products.

关键词： snow identification random forest Fengyun-3B Visible and Infra-Red Radiometer feature selection

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Revisiting BCC-SESM parameters sensitivity with BCC-CSM1.1 co_(2)-concentration-driven simulations

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Advances in Climate Change Research 2022年第3期13卷 301-308页

作者： Chang-Yi Liu Ying Lu Xue-Li Shi Guo-Quan Hu Qiu-Feng Liu Chao Wei Yun-Fei Cao National Climate centre China Meteorological Administration(CMA)Beijing 100081China Global Energy Interconnection Group Corporation(GEIDCO) Beijing 100031China North China Electric Power University(Baoding) Baoding 071003China cma earth system modeling and prediction centre Beijing 100081China Center for Energy&Environmental Policy Research Beijing Institute of TechnologyBeijing 100081China

Based on the results of the complex climate model BCC-CSM,the Beijing Climate Center Simple earth system Model(BCC-SESM)was developed for climate system simulations in Integrated Assessment Models(IAMs).The first version of the BCC-SESM model was based on a high-emissions scenario(ESMRCP8.5)and tends to overestimate the temperatures in low and medium emissions *** address this problem,this study uses three CO_(2)-concentration-driven simulations under different RCP scenarios of complex climate models to evaluate parameters sensitivity and their impacts on projection *** results show that the new version of the BCC-SESM(denoted as BCC-SESM1.1)model based on a medium-emissions scenario experiment(RCP4.5)is more suitable for temperature projections for various climate *** can well reproduce the original value of complex climate *** the same time,it also has high predictive efficacies for medium(RCP4.5)and low(RCP2.6)emissions scenarios,although it tends to underestimate for high emissions scenario(RCP8.5).The sensitivity tests for different RCP scenarios shows that the BCC-SESM1.1 has higher efficacy in projections of future climate change than those model versions based on the other *** projection deviations for the global average temperature by the BCC-SESM1.1(<2%)are better than the previous BCC-SESM(<5%).In light of recent progress in climate policy,the BCC-SESM1.1 is hence more suitable for coupling with IAMs for the purposes of assessing climate outcomes.

关键词： Simple climate model Integrated assessment models BCC-CSM1.1 CMIP5 BCC-SESM

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Spatial Inhomogeneity of Atmospheric CO_(2) Concentration and Its Uncertainty in CMIP6 earth system Models

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Advances in Atmospheric Sciences 2023年第11期40卷 2108-2126页

作者： Chengjun XIE Tongwen WU Jie ZHANG Kalli FURTADO Yumeng ZHOU Yanwu ZHANG Fanghua WU Weihua JIE He ZHAO Mengzhe ZHENG Chinese Academy of Meteorological Sciences China Meteorological AdministrationBeijing 100081China University of Chinese Academy of Sciences Beijing 100049China cma earth system modeling and prediction centre Beijing 100081China State Key Laboratory of Severe Weather Beijing 100081China Met Office Hadley centre ExeterEX13PBUK

This paper provides a systematic evaluation of the ability of 12 earth system Models(ESMs)participating in the Coupled Model Intercomparison Project Phase 6(CMIP6)to simulate the spatial inhomogeneity of the atmospheric carbon dioxide(CO_(2))*** multi-model ensemble mean(MME)can reasonably simulate the increasing trend of CO_(2) concentration from 1850 to 2014,compared with the observation data from the Scripps CO_(2) Program and CMIP6 prescribed data,and improves upon the CMIP5 MME CO_(2) concentration(which is overestimated after 1950).The growth rate of CO_(2) concentration in the northern hemisphere(NH)is higher than that in the southern hemisphere(SH),with the highest growth rate in the mid-latitudes of the *** MME can also reasonably simulate the seasonal amplitude of CO_(2) concentration,which is larger in the NH than in the SH and grows in amplitude after the 1950s(especially in the NH).Although the results of the MME are reasonable,there is a large spread among ESMs,and the difference between the ESMs increases with *** MME results show that regions with relatively large CO_(2) concentrations(such as northern Russia,eastern China,Southeast Asia,the eastern United States,northern South America,and southern Africa)have greater seasonal variability and also exhibit a larger inter-model *** with CMIP5,the CMIP6 MME simulates an average spatial distribution of CO_(2) concentration that is much closer to the site observations,but the CMIP6-inter-model spread is *** inter-model differences of the annual means and seasonal cycles of atmospheric CO_(2) concentration are both attributed to the differences in natural sources and sinks of CO_(2) between the simulations.

关键词： CMIP6 earth system Models the simulation of atmospheric CO_(2)concentration spatial inhomogeneity uncertainty

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Impacts of the Urban Spatial Landscape in Beijing on Surface and Canopy Urban Heat Islands

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Journal of Meteorological Research 2022年第6期36卷 882-899页

作者： Yonghong LIU Yongming XU Yeping ZHANG Xiuzhen HAN Fuzhong WENG Chunyi XUAN Wenjun SHU cma earth system modeling and prediction centre China Meteorological Administration(CMA)Beijing 100081 State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesChina Meteorological AdministrationBeijing 100081 School of Remote Sensing and Geomatics Engineering Nanjing University of Information Science&TechnologyNanjing 210044 National Satellite Meteorological centre China Meteorological AdministrationBeijing 100081 Beijing Municipal Climatecentre Beijing Meteorological ServiceBeijing 100089

How does the urban spatial landscape(USL)pattern affect the land surface urban heat islands(SUHIs)and canopy urban heat islands(CUHIs)?Based on satellite and meteorological observations,this case study compares the impacts of the USL pattern on SUHI and CUHI in the central urban area(CUA)of Beijing using the satellite land-surface-temperature product and hourly temperature data from automatic meteorological stations from 2009 to *** USL metrics—building height(BH),building density(BD),standard deviation of building height(BSD),floor area ratio(FAR),frontal area index(FAI),roughness length(RL),sky view factor(SVF),urban fractal dimension(FD),vegetation coverage(VC),impervious coverage(IC),and albedo(AB)—with a 500-m spatial resolution in the CUA are extracted for comparative *** results show that SUHI is higher than CUHI at night,and SUHI is only consistent with CUHI at spatial-temporal scales at night,particularly in ***,all 11 metrics are strongly correlated with both the SUHI and CUHI at night,with stronger correlation between most metrics and ***,AB,and SVF have the greatest impact on both the SUHI and *** SUHI and CUHI values tend to appear in areas with BD≥0.26,VC≤0.09,AB≤0.09,and SVF≤*** summer,most metrics have a greater impact on the SUHI than CUHI;the opposite is observed in *** variation is affected primarily by VC in summer and by VC and AB in winter,which is different for the CUHI *** collective contribution of all 11metrics to SUHI spatial variation in summer(61.8%)is higher than that to CUHI;however,the opposite holds in winter and for the entire year,where the cumulative contribution of the factors accounts for 66.6%and 49.6%,respectively,of the SUHI variation.

关键词： surface urban heat island canopy urban heat island vegetation coverage albedo sky view factor building density Beijing

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Ground–Space–Sky Observing system Experiment during Tropical Cyclone Mulan in August 2022

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Advances in Atmospheric Sciences 2023年第2期40卷 194-200页

作者： Pak-wai CHAN Wei HAN Betty MAK Xiaohao QIN Yongzhu LIU Ruoying YIN Jincheng WANG Hong Kong Observatory Hong KongChina China Meteorological Administration earth system modeling and prediction centre Beijing 100081China State Key Laboratory of Numerical modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China

Forecasting tropical cyclone track and intensity is a great challenge for the meteorological community,and safeguarding the life and property of people living near the coast is an important *** major reason for challenging forecasts is the lack of observations over the vast *** tropical cyclone Mulan between 8 and 10 August 2022 over the northern part of the South China Sea,the meteorological authority and research institutes of Chinese mainland collaborated with the meteorological service in Hong Kong on conducting the first-ever ground–space–sky observing system experiment on tropical cyclone *** enhanced targeted observations collected during the experiment include Geostationary Interferometric Infrared Sounder,round-trip radiosondes,and aircraft-launched *** paper describes the campaign,technical details of the meteorological models used,and impact of the additional targeted observation data on the tropical cyclone ***,similar enhanced observation campaigns could be conducted in the future,not only in the northern part of the South China Sea,but also in other ocean basins.

关键词： meteorological cyclone tropical

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Assimilation of Ocean Surface Wind Data by the HY-2B Satellite in GRAPES: Impacts on Analyses and Forecasts

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Advances in Atmospheric Sciences 2023年第1期40卷 44-61页

作者： Jincheng WANG Xingwei JIANG Xueshun SHEN Youguang ZHANG Xiaomin WAN Wei HAN Dan WANG cma earth system modeling and prediction Center China Meteorological AdministrationBeijing 100081China National Meteorology Center China Meteorological AdministrationBeijing 100081China Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou) Guangzhou 511458China National Satellite Ocean Application Service Beijing 100081China

The ocean surface wind(OSW)data retrieved from microwave scatterometers have high spatial accuracy and represent the only wind data assimilated by global numerical models on the ocean surface,thus playing an important role in improving the forecast skills of global medium-range weather prediction *** improve the forecast skills of the Global/Regional Assimilation and prediction system Global Forecast system(GRAPES_GFS),the HY-2B OSW data is assimilated into the GRAPES_GFS four-dimensional variational assimilation(4DVAR)***,the impacts of the HY-2B OSW data assimilation on the analyses and forecasts of GRAPES_GFS are analyzed based on one-month assimilation cycle *** results show that after assimilating the HY-2B OSW data,the analysis errors of the wind fields in the lower-middle troposphere(1000-600 hPa)of the tropics and the southern hemisphere(SH)are significantly reduced by an average rate of about 5%.The impacts of the HY-2B OSW data assimilation on the analysis fields of wind,geopotential height,and temperature are not solely limited to the boundary layer but also extend throughout the entire troposphere after about two days of cycling ***,assimilating the HY-2B OSW data can significantly improve the forecast skill of wind,geopotential height,and temperature in the troposphere of the tropics and SH.

关键词： HY-2B ocean surface wind 4DVAR GRAPES-GFS medium-range weather forecast

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Spatial and temporal variations of gross primary production simulated by land surface model BCC_AVIM2.0

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Advances in Climate Change Research 2023年第2期14卷 286-299页

作者： Wei-Ping LI Yan-Wu ZHANG Mingquan MU Xue-Li SHI Wen-Yan ZHOU Jin-Jun JI earth system modeling and prediction centre China Meteorological AdministrationBeijing 100081China State Key Laboratory of Severe Weather(LaSW) China Meteorological AdministrationBeijing 100081China Department of earth system Science University of CaliforniaIrvine CA 92697-3100USA Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China

Gross primary production(GPP)is the largest flux and a crucial player in the terrestrial carbon cycle and has been studied extensively,yet large uncertainties remain in the spatiotemporal patterns of GPP in both observations and *** study evaluates the performance of the second version of the Beijing Climate Center Atmosphere−Vegetation Interaction Model(BCC_AVIM2.0)in simulating GPP on multiple spatial and temporal scales in the Coupled Model Intercomparison Project Phase 6(CMIP6)*** simulations driven by two meteorological datasets were compared with two observation-based GPP products covering 1982–*** patterns of annual GPP show a significant latitudinal gradient in each dataset,increasing from cold(tundra)and dry(desert)biomes to warm(temperate)and humid(tropical rainforest)***_AVIM2.0 overestimates GPP in most parts of the globe,especially in boreal forest regions and Southeast China,while underestimating GPP in subhumid regions in eastern South America and tropical *** four datasets broadly agree on the GPP seasonal cycle,but BCC_AVIM2.0 predicts an earlier beginning of spring growth and a larger amplitude of seasonal variations than those in the *** observation-based datasets exhibit slight interannual variability(IAV)and weak GPP linear trends,while the BCC_AVIM2.0 simulations demonstrate relatively large year-to-year variability and significant trends in the low-latitudes and temperate monsoon regions in North America and East *** the possible relationships between annual means of GPP and climate factors,BCC_AVIM2.0 predicts more extensive regions of the globe where the IAV of annual GPP is dominated by precipitation,especially in mid-to-high latitudes of the Northern Hemisphere and tropical Africa,while the observed GPP in the above regions is temperature-or *** positive GPP biases due to earlier spring growth in boreal forest regions and negative GPP biases in off

关键词： Gross primary production Seasonal cycle Interannual variability Trend Land surface model CMIP6

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Blacklist Design of AMDAR Temperature Data and Their Application in the cma-GFS

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Journal of Tropical Meteorology 2021年第4期27卷 368-377页

作者：王瑞文韩威田伟红龚建东 cma earth system modeling and prediction centre Beijing 100081 China State Key Laboratory of Severe Weather Beijing 100081 China

Blacklist methods are used in the cma Global Forecasting system(cma-GFS)to improve the application of aircraft temperature data to numerical weather prediction in the Northern Hemisphere and the *** this paper,the ERA5 re-analysis data are used to analyze aircraft temperature observation errors of each aircraft and a blacklist is established using pre-quality controls and threshold *** blacklist-filtered and blacklisted aircraft temperature data are then applied to the four-dimensional variational assimilation system,respectively,and an assimilation cycle forecast for the period from September 1 to 30,2019 is carried *** results show an uneven distribution in the global aircraft blacklist *** the application of the blacklist methods,the RMSE of geopotential height and temperature analysis field decrease in the vertical direction by a maximum of~1.5 gpm at 200 hPa and~0.15 K at 250 hPa,***,the blacklist methods of aircraft temperature data improve the analysis and forecast in the cma-GFS.

关键词： cma-GFS blacklist AMDAR four-dimensional variational assimilation

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Assimilation of FY-3D MWTS-Ⅱ Radiance with 3D Precipitation Detection and the Impacts on Typhoon Forecasts

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Advances in Atmospheric Sciences 2023年第5期40卷 900-919页

作者： Luyao QIN Yaodeng CHEN Gang MA Fuzhong WENG Deming MENG Peng ZHANG Key Laboratory of Meteorological Disaster of Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science&TechnologyNanjing 210044China earth system modeling and prediction centre China Meteorological AdministrationBeijing 100081China Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites(LRCVES/cma) National Satellite Meteorological CenterChina Meteorological Administration(NSMC/CMA)Beijing 100081China

Precipitation detection is an essential step in radiance assimilation because the uncertainties in precipitation would affect the radiative transfer calculation and observation *** traditional precipitation detection method for microwave only detects clouds and precipitation horizontally,without considering the three-dimensional distribution of *** precipitation detection from 2D to 3D is expected to bring more useful information to the data assimilation without using the all-sky *** this study,the 3D precipitation detection method is adopted to assimilate Microwave Temperature Sounder-2(MWTS-Ⅱ)onboard the Fengyun-3D,which can dynamically detect the channels above precipitating clouds by considering the near-real-time cloud *** data assimilation and forecasting experiments for Typhoons Lekima(2019)and Mitag(2019)are carried *** with the control experiment,the quantity of assimilated data with the 3D precipitation detection increases by approximately 23%.The quality of the additional MWTS-Ⅱradiance data is close to the clear-sky *** case studies show that the average root-mean-square errors(RMSE)of prognostic variables are reduced by 1.7%in the upper troposphere,leading to an average reduction of4.53%in typhoon track *** detailed diagnoses of Typhoon Lekima(2019)further show that the additional MWTS-Ⅱradiances brought by the 3D precipitation detection facilitate portraying a more reasonable circulation situation,thus providing more precise *** paper preliminarily proves that 3D precipitation detection has potential added value for increasing satellite data utilization and improving typhoon forecasts.

关键词： numerical weather prediction radiance assimilation microwave temperature sounding FY-3D MWTS-II precipitation detection

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