摘要
对强雷暴的预报一直以来都是天气预报的重点和难点。先进的集合资料同化方法能够减小中小尺度数值天气预报的初始场中的误差,以此提高对强雷暴天气的预报的准确程度。本文简要地介绍了集合资料同化方法的原理及其变体,提高集合资料同化方法的分析场质量的多种方案,不同观测平台的数据在强雷暴天气集合资料同化系统中的应用,已经开始业务化或准业务化持续运行的一些着眼于强雷暴天气预报的集合资料同化系统,强雷暴天气集合资料同化应用中目前面临的一些问题和未来可能的发展方向。
Predicting severe convective storms has long been recognized as one of the most important aspects as well as one of the most difficult part of weather forecast. With advanced ensemble-based data assimilation techniques like ensemble Kalman filter, the uncertainties in initial conditions of storm-scale weather prediction can be significantly reduced, leading to an improved performance of severe weather forecast. This review paper will briefly introduce the concepts and variations of ensemble Kalman filter, schemes to improve filter performance at storm scales, applications of various conventional and in situ observational platforms in ensemble data assimilation, and focuses on the severe weather prediction systems as well as operational and quasi-operational storm-scale ensemble data assimilation and prediction systems, the issues and difficulties that encountered in current applications, and possible future directions.
作者
张云济
张福青
Zhang Yunji;Zhang Fuqing(Center for Advanced Data Assimilation and Predictability Techniques,Department of Meteorology and Atmospheric Sciences,Pennsylvania State University,Pennsylvania 16802)
出处
《气象科技进展》
2018年第3期38-52,共15页
Advances in Meteorological Science and Technology
关键词
资料同化
集合卡尔曼滤波
雷达资料
强对流雷暴
data assimilation
ensemble Kalman flter
radar data
severe convective storm
