报告题目：Water Vapor for Precipitation Forecasting: Observations from GNSS, Remote Sensing, and 5G and their Assimilation⎯⎯from aGeomatician’sPerspective
报 告 人：刘志赵 教授
报告时间：2025年12月1日（周一）9:30
报告地点：气象楼1114会议室
主 持 人：杨元建 教授
报告人简介：
刘志赵，男，现为香港理工大学土地测量及地理资讯学系教授，兼任中国气象局气象探测中心首席科学家，香港天文台科学顾问，加拿大测绘地理信息学会香港分会创会主席。他领导的Micro-Laboratory of Atmospheric Research and Geomatics Engineering (Micro-LARGE)实验室在GNSS卫星精密定位导航技术、应用GNSS及遥感卫星监测大气水汽和电离层变化，卫星遥感水汽数据的反演、订正、数据同化等方面有独特的发展创新。多个GNSS定位导航技术基础算法被国内外研究机构和工业界广泛应用，Micro-LARGE是国际上第一个实现全天候（包括有云情况）近红外NIR波段卫星遥感水汽数据订正的实验室，可以降低水汽误差20%～60%,也是国际上第一个在数值天气预报模式中成功同化全天候近红外NIR大气可降水汽数据的实验室。他曾获得香港政府研究资助局2012年首届“杰出青年学者奖”，曾任全球华人导航定位协会（CPGPS）会长，与江西师范大学林珲教授共同发起“思本论坛”。他是江西理工大学、武汉大学、加拿大卡尔加里大学的毕业生。
报告简介：
“We have more water vapor in the atmosphere, which leads to extreme rainfall and deadly flooding” (Dr. Jukka Petteri Taalas, Former Secretary-General of WMO). Accurate observations of water vapor have long been a challenge in the atmospheric science society. In this talk, we will discuss multiple methods of accurately retrieving water vapor from different technologies, including GPS/GNSS satellite signals, remote sensing satellites, and 5th-Generation (“5G”) Cellular Network Stations. Particularly we will focus on the development of machine learning methods to retrieve water vapor from remote sensing satellites (including Chinese Fengyun satellites). The accuracy of retrieved water vapor data is 20% to 60% higher than that from the satellite water vapor products themselves.Finally, water vapor data from GNSS, satellites, and 5G stations have been assimilated into Numerical Weather Prediction model to demonstrate its effectiveness in extreme precipitation studies. A number of extreme rainfall studies for in South China region have shown that assimilating water vapor data can significantly improve the scores forecasting skills.To the best of our knowledge, we are arguably the 2nd team in the world to assimilate Near-infrared (NIR) satellite water vapor into NWP model, after the U.K. Met Office which is the world’s first agency to operationally assimilate satellite NIR water vapor into its global NWP model since May 2022. Different from the U.K. Met Office, the NIR water vapor dataset we assimilate has been calibrated using our Lab’s new approach thus it has a better accuracy.
欢迎广大师生踊跃参加！
大气物理学院
2025年11月27日
