基于FY-3C MWHTS的台风降水反演算法研究

Research on Typhoon Precipitation Retrieval Algorithm based on FY-3C MWHTS

为估测台风带来的地表瞬时降雨率,利用FY-3C上搭载的微波湿温探测仪(Microwave Humidity and Temperature Sounder,MWHTS)的L1级在轨观测亮度温度数据与多卫星降水分析TMPA(Tropical Rainfall Measuring Mission(TRMM)Multi-Satellite Precipitation Analysis)3B42降水产品数据,通过多元线性回归和BP神经网络两种算法对台风区的降水情况进行了反演研究。结果表明,由这两种算法反演的降水分布图可以清晰地看到台风中心、云墙以及螺旋雨带等台风的位置、分布及结构信息,这与TMPA 3B42降水产品数据估测到的台风降水分布图相一致。此外,从定量的角度来看,TMPA 3B42降水数据与这两种反演算法反演的地表瞬时降水量(mm/hr)都具有较高的相关性和较小的偏差和均方根误差,反演的精度较高。故这两种算法都可以用来反演台风区的降水量,同时也表明FY-3C MWHTS微波在轨观测资料在台风区监测及降水研究中能发挥出较高的应用价值。

In order to estimate the instantaneous precipitation rates brought by the typhoon,the Level 1 brightness temperatures from the Microwave Humidity and Temperature Sounder(MWHTS)onboard the FY-3 C satellite and the Tropical Rainfall Measuring Mission(TRMM)Multi-Satellite Precipitation Analysis(TMPA)3 B42 precipitation product data are used to retrieve the precipitation rates in the typhoon area using the multiple linear regression and BP neural network retrieval algorithms. The results show that the precipitation distribution maps retrieved by these two algorithms can be clearly observed the location,distribution and structural information of the typhoons such as typhoon center,cloud wall and spiral rain belt,which are consistent with the TMPA 3 B42 precipitation product data. In addition,from a quantitative point of view,the TMPA3 B42 precipitation data and surface precipitation rate(mm/hr)retrieved by these two precipitation retrieval algorithms reach higher correlation and smaller deviations and root mean square errors,and the retrieval accuracy is higher. Therefore,these two retrieval algorithms can be used to retrieve the precipitation in the typhoon area.It also shows that microwave on-orbit observation data from the FY-3 C MWHTS can play a high application value in typhoon monitoring and precipitation research.