@article{LI2024, 
author = {Jun LI and Jing ZHENG and Bo LI and Min MIN and Yanan LIU and Chian-Yi LIU and Zhenglong LI and W. Paul MENZEL and Timothy J. SCHMIT and John L. CINTINEO and Scott LINDSTROM and Scott BACHMEIER and Yunheng XUE and Yayu MA and Di DI and Han LIN},
title = {Quantitative Applications of Weather Satellite Data for Nowcasting: Progress and Challenges},
year = {2024},
journal = {Journal of Meteorological Research},
volume = {38},
number = {3},
pages = {399-413},
keywords = {weather satellite, quantitative applications, nowcasting, pre-convection},
url = {https://www.sciopen.com/article/10.1007/s13351-024-3138-6},
doi = {10.1007/s13351-024-3138-6},
abstract = {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 action. Nowcasting these events usually relies on radar observations through monitoring and extrapolation. With 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 techniques. Those 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 storm. Satellite observations can provide information on the environmental characteristics in the pre-convection stage and are very useful for situational awareness and storm warning. This 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.}
}