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 Missi...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.展开更多
基金National Key Research and Development Program of China(2017YFC1404002)National Natural Science Foundation of China(41405001,U1502233)
文摘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.
