COMPARISON OF PERSIANN AND TMPA DAILY PRECIPITATION ESTIMATES OVER HUNAN PROVINCE OF CHINA

This study presented a detailed comparison of daily precipitation estimates from Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) and Tropical Rainfall Measuring Mission (TRMM) Multi -satellite Precipitation Analysis (TMPA) over Hunan province of China from 1998 to 2014. The ground gauge observations are taken as the reference. It is found that overall TMPA clearly outperforms PERSIANN, indicating by better statistical metrics (including correlation coefficient, root mean square error and relative bias). For the geospatial pattern, although both products are able to capture the major precipitation features (e.g., precipitation geospatial homogeneity) in Hunan, yet PERSIANN largely underestimates the precipitation intensity throughout all seasons. In contrast, there is no clear bias tendency from TMPA estimates. Precipitation intensity analysis showed that both the occurrence and amount histograms from TMPA are closer to the gauge observations from spring to autumn. However, in the winter season PERSIANN is closer to gauge observation, which is likely due to the ground contamination from the passive microwave sensors used by TMPA.     

超强台风"桑美"的特点及其成因分析

2006年第8号台风"桑美"(0608,SAOMAI)是1949年以来登陆我国大陆最强的台风,登陆时中心附近最大风力达到17级(60m.-s1),最低气压为920hPa,利用常规探测资料、风云2C红外云图及T 213资料,对其观测事实进行分析探讨,结果发现,"桑美"具有强度强、移速快、结构紧密,"个头"小、风速大、雨势猛、灾害重等特点,这些特点与大气环流的演变、影响台风的天气系统、相关物理量的分布、"桑美"本身的结构及其结构变化等有着十分密切的关系。