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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. 相似文献
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