随着风云三号系列卫星的成功发射,越来越多的卫星微波直接观测资料应用于数值天气预报的资料同化系统.并且由于卫星微波全天候同化技术可以充分利用晴天及云雨区微波观测资料,在增加同化使用的观测数据的基础上,有效提高数值天气预报准确率,该技术在卫星资料同化领域也颇受瞩目.本研究选取 2018年 7月的台风玛莉亚,利用WRF(Weather Research and Forecasting)模式及其同化系统WRFDA(WRF Data Assimilation)中三维变分方法,探讨风云三号C星微波湿度计观测资料的全天候同化技术在区域模式中的适用性,以及其在不同模式驱动场中的预报表现.通过对比仅同化晴空区域卫星资料的试验和全天候同化的试验结果发现,全天候条件下更多的云雨区域观测资料被有效利用,能够更好地模拟出台风玛莉亚核心区域的暖心和对称风速结构,有效改善湿度场的预报,对台风路径的预报误差平均降低了大约 34%～62%,且这种正面影响均能在不同模式驱动场中得到体现.
Abstract
The successful launch of FengYun-3 has enabled more satellite observations of radiances to be directly assimilated.The introduction of satellite microwave all-sky assimilation technology enhances the utilization of microwave observation data under clear,cloudy,and precipitation conditions.This advancement not only increases assimilated radiances but also improves numerical weather prediction.Consequently,the all-sky approach has received considerable attention recently.In this study,typhoon Maria occurred in July 2018 is studied.In particularly,the all-sky assimilation of FY-3C/MWHS-2(FengYun-3C/MicroWave Humidity Sounders)data was performed using the Weather Research and Forecasting(WRF)Model based on the three-dimensional variational data assimilation technique implemented in the WRF data assimilation system.The objective was to study the effect of assimilating FY-3C/MWHS-2 data under all-sky conditions and how different types of initial field data affect forecast accuracy in the regional numerical model.Results of clear-sky and all-sky assimilation experiments show that under all-sky conditions,more clouds and precipitation data are assimilated.Improvements were observed in the analysis of the vertical structures(such as the warm core and symmetric wind speed)and humidity fields of typhoon Maria.The average track-error reduction rates ranged from 34%to 62%.These positive effects can be demonstrated in all all-sky experiments utilizing different data types for initializing the WRF model.
关键词
风云三号C星/微波湿度计/全天候同化/台风预报
Key words
FengYun-3C satellite/Microwave humidity sounders/All-sky data assimilation/Forecasts of typhoon
维普
万方数据