基于温湿廓线反演的风云三号D星微波探测仪云区资料同化试验

为了探究云区卫星微波资料的同化应用，利用中国风云三号D星(Fengyun-3D,FY-3D)微波温度计二型(microwave temperature sounder-2,MWTS2)和微波湿度计二型(microwave humidity sounder-2, MWHS2)资料，基于人工神经网络算法研制了云区温湿廓线反演模型，建立了云区资料的间接同化方案。于2019年6月开展晴空和云区同化试验，评估加入云区MWTS2和MWHS2资料对区域模式预报的影响。试验结果表明：MWTS2和MWHS2资料的同化对温湿度预报场有改善，主要体现在模式中高层均方根误差和平均偏差的减小，云区同化的改善幅度比晴空同化更大；同化MWTS2和MWHS2资料对于提高降水预报技巧有积极影响，云区同化对降水预报的改善主要体现在同化后的12～24 h,较晴空同化更明显。针对强降水个例分析表明，MWTS2和MWHS2资料对温度场、湿度场、水汽通量散度场的调整有利于降水预报的改善，而云区同化能够直接对天气系统的初始场进行调整，降水的位置与强度预报效果更好。

Experimental research of FY-3D microwave detector cloudy data assimilation based on temperature and humidity profile retrieval

In order to explore the assimilation application of cloud-affected satellite microwave observations, in this study the inversion model of atmospheric temperature and humidity profile in cloudy condition based on the artificial neural network algorithm was developed by using FY-3D Microwave Temperature Sounder-2 (MWTS2) and Microwave Humidity Sounder-2 (MWHS2) data and the indirect assimilation scheme of cloudy data was established. One-month continuous experiments and real-case study about clear-sky and cloudy assimilation were performed in June 2019 to illustrate the influence of MWTS2 and MWHS2 cloudy observations on regional numerical weather prediction model. The results show that: The assimilation of MWTS2 and MWHS2 data improves the predicted temperature and humidity field, which is mainly reflected in the reduction of bias and root mean square error in the high layer of the model. The improvement rate of cloudy assimilation is higher than that of clear-sky assimilation. Assimilating MWTS2 and MWHS2 data has a positive effect on improving precipitation prediction skills. The improvement of cloudy assimilation on precipitation prediction is mainly reflected in 12-24 hours after assimilation, which is more obvious than that of clear-sky assimilation. The real-case study of heavy precipitation shows that the adjustment of temperature, humidity and moisture flux divergence field by MWTS2 and MWHS2 data is conducive to improve precipitation forecast skill, while cloudy assimilation can adjust the initial field of weather system directly, having better prediction effect of precipitation position and intensity.