Comparative assessment between the daily merged precipitation dataset over China and the world's popular counterparts

The two-step merging algorithm called the Probability Density Function and Optimal Interpolation (PDF-OI) technique has been applied to create a China Merged Precipitation Analysis at a daily and 0.25° resolution (CMPA_Daily) since 1998 which is quality-controlled based on more than 2,400 gauge observations over China and the CPC (Climate Prediction Center, NOAA) Morphing (CMORPH) satellite QPE. The world widely-used datasets of TRMM 3B42 (Tropical Rainfall Measuring Mission, 3B42) and GPCP (Global Precipitation Climatology Project, 1 degree daily) are introduced to conduct comparative analysis over the China domain. The errors of CMPA_Daily, TRMM 3B42 and GPCP are calculated based on the independent validation data from May to September, 2008-2010. The results show that CMPA_Daily has the highest correlation coefficient and the lowest Bias and Root-Mean-Square Error both in temporal variations and spatial distribution while GPCP has the relatively higher errors among the three products. Underestimation of heavy storm only, heavy rain or above, and moderate rain or above is observed by CMPA_Daily, TRMM 3B42 and GPCP, respectively. For the rainfall rate higher than 25 mm/d, the relative Bias is within 10%, 30% and 60% for CMPA_Daily, TRMM 3B42 and GPCP, respectively. The precision of CMPA_Daily is observably promoted no matter in the eastern coastal areas of China or in the mid-western complex terrain compared with that of both the TRMM 3B42 and GPCP as a result of merging more gauge observations. Even over the sparse observations area, such as the Tibetan Plateau, the precipitation value from CMPA_Daily is closer to the gauge observations. The high correlation between CMPA_Daily and gauge-based precipitations is stable even in areas with great hypsography, while the correlation coefficients for TRMM 3B42 and GPCP are falling obviously with topographic changes. At seasonal and annual timescales, the three datasets for 1998-2012 can be matched very well to represent the spatial distribution of precipitation while the CMPA_Daily dataset can give more detailed information owing to more gauges used. The precipitation special distribution structure from CMPA_Daily over western China is similar to the satellite products and more reasonable than the interpolation gauge-based precipitation products, closing to the satellite-merged precipitation products such as TRMM 3B42 and GPCP. For the monitoring of severe rainfall event, compared with TRMM 3B42 and GPCP_1DD, the CMPA_Daily dataset can exhibit more accurate precipitation distribution value and structure. The TRMM 3B42 dataset can give precipitation distribution structure well but tends to underestimate the precipitation amount. Because of relatively coarse resolution, GPCP can't show the position and amount of local precipitation very well.