基于TRMM和FY-2C的长江流域月降水量的网格化估算研究

基于TRMM和FY-2C降水产品,结合气象站观测资料和DEM数据,采用基于区域分月的逐步回归算法,建立降水估算模型。通过模型估算得到长江流域2007年1月、4月、7月、10月降水量的空间分布图,并对得到的结果进行检验和分析。结果表明:模型对2种降水产品能进行有效的订正;模型估算的TRMM降水产品1月、4月、7月、10月的平均相对误差分别是37.7%,47.3%,44.2%,41.9%,FY-2C降水产品1月、4月、7月、10月的平均相对误差是46.3%,50.9%,39.8%,48.8%;模型模拟的TRMM降水的全年相关系数是0.838, FY-2C降水的全年相关系数是0.811,通过两者对比发现,TRMM降水产品作为趋势项的精度较高。模型估算得到的降水分布趋势和原始降水产品分布趋势基本一致,并且能体现出降水的分布规律。

Grid Estimation of Monthly Precipitation in the Yangtze River Basin Based on TRMM and FY-2C

In this paper, we adopted a stepwise regression algorithm based on regional monthly division to establish precipitation estimation model based on the data from TRMM and FY-2C. This model also combined with observation data of weather stations and DEM data. By employing the estimation model, we obtained the spatial distribution of precipitation of the Yangtze River in January, April, July and October 2007, and tested and analyzed the results. The simulation results showed that the model could revise TRMM and FY-2C effectively. Further analysis and calculation showed that the averaged relative errors of TRMM precipitation in the four months were 37.7%, 47.3%, 44.2% and 41.9%, respectively,while those of FY-2C precipitation were 46.3%, 50.9%, 39.8% and 48.8%, respectively. From the perspective of the whole year, the correlation coefficient of simulated TRMM precipitation was 0.838, while the correlation coefficient of simulated FY-2C precipitation was 0.811. The simulation result showed that TRMM was more accurate than FY-2C. Moreover, the distribution of precipitation remained almost the same with the original data, and the results of the present model reflected the distribution pattern of precipitation.