Multi-scale evaluation of high-resolution multi-sensor blended global precipitation products over the Yangtze River

In the present study, four high-resolution multi-sensor blended precipitation products, TRMM Multisatellite Precipitation Analysis (TMPA) research product (3B42 V7) and near real-time product (3B42 RT), Climate Prediction Center MORPHing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), are evaluated over the Yangtze River basin from April 2008 to March 2012 using the gauge data. This regional evaluation is performed at temporal scales ranging from annual to daily, based on a number of diagnostic statistics. Gauge adjustment greatly reduces the bias in 3B42 V7, a post real-time research product. Additionally, it helps the product maintain a stable skill level in winter. When additional indicators such as spatial correlation, Root Mean Square Error (RMSE), and Probability of Detection (POD) are considered, 3B42 V7 is not always superior to other products (especially CMORPH) at the daily scale. Among the near real-time datasets, 3B42 RT overestimates annual rainfall over the basin; CMORPH and PERSIANN underestimate it. In particular, the upper Yangtze always suffers from positive bias (>1 mm day(-1)) in the 3B42 RT dataset and negative bias (-0.2 to 1 mm day-1) in the CMORPH dataset. When seasonal scales are considered, CMORPH exhibits negative bias, mainly introduced during cold periods. The correlation between CMORPH and gauge data is the highest. On the contrary, the correlation between 3B42 RT and gauge data is more scattered; statistically, this results in lower bias. Finally, investigation of the probability distribution functions (PDFs) suggests that 3B42 V7 and 3842 RT are consistently better at retrieving the PDFs in high-intensity events. Overall, this study provides useful information about the error characteristics associated with the four mainstream satellite precipitation products and their implications regarding hydrological applications over the Yangtze River basin. (C) 2013 Elsevier B.V. All rights reserved.