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Quantitative Applications of Weather Satellite Data for Nowcasting:Progress and Challenges

Journal of Meteorological Research 2024年第3期38卷 399-413页

作者： Jun LI Jing ZHENG Bo LI Min MIN Yanan LIU Chian-Yi LIU Zhenglong LI WPaul MENZEL Timothy J.SCHMIT John L.CINTINEO scott lindstrom scott BACHMEIER Yunheng XUE Yayu MA Di DI Han LIN Innovation Centre for Fengyun Meteorological Satellite(FYSIC) National Satellite Meteorological CentreChina Meteorological AdministrationBeijing100081China School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies Sun Yat-sen University and Southern Laboratory of Ocean Science and EngineeringZhuhai519082China Key Laboratory of Geographic Information Science(Ministry of Education) East China Normal UniversityShanghai200241China Research Center for Environmental Changes Academia SinicaTaipei11529China Cooperative Institute for Meteorological Satellite Studies University of Wisconsin-MadisonMadisonWI53706USA Sensors and Calibration Branch STAR/NESDISMadisonWI53706USA Wuxi University Wuxi214105China School of Computer Science Chengdu University of Information TechnologyChengdu610225China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science&TechnologyNanjing210044China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National and Local Joint Engineering Research Center of Satellite Geospatial Information TechnologyFuzhou UniversityFuzhou350108China

Monitoring and predicting highly localized weather events over a very short-term period,typically ranging from minutes to a few hours,are very important for decision makers and public *** these events usually relies on radar observations through monitoring and *** advanced high-resolution imaging and sounding observations from weather satellites,nowcasting can be enhanced by combining radar,satellite,and other data,while quantitative applications of those data for nowcasting are advanced through using machine learning *** applications include monitoring the location,impact area,intensity,water vapor,atmospheric instability,precipitation,physical properties,and optical properties of the severe storm at different stages(pre-convection,initiation,development,and decaying),identification of storm types(wind,snow,hail,etc.),and predicting the occurrence and evolution of the *** observations can provide information on the environmental characteristics in the preconvection stage and are very useful for situational awareness and storm *** paper provides an overview of recent progress on quantitative applications of satellite data in nowcasting and its challenges,and future perspectives are also addressed and discussed.

关键词： weather satellite quantitative applications nowcasting pre-convection

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