资源环境科技发展态势分析平台

This article presents a detailed comparison of ten precipitation products over the central southwest Asia (CSWA) region. The spatial characteristics and temporal variations of wintertime precipitation over CSWA region are assessed using four gauge-only [Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Climate Prediction Center Unified Rain gauge (CPC-uni), Global Precipitation Climatology Center Full Data Reanalysis (GPCC) and Climate Research Unit (CRU)]; three satellite-derived (3B42-V6, 3B42-V7 and GPCP-1DD) and three reanalysis [Climate Forecast System Reanalysis (CFSR), ERA-Interim and Modern-Era Retrospective Analysis for Research and Applications (MERRA)] precipitation products. The analysis is performed for two different periods: (1979-2007) for the gauge-only/reanalyses and (1998-2007) for the satellite-derived products. Using an ensemble average of four gauge-only observational data sets as our reference, we carry out comprehensive qualitative assessment of the uncertainties/biases associated with each data set. Additionally, we examine the relationship pattern between CSWA wintertime precipitation and El Nino-Southern Oscillation (ENSO) phases. In the gauge-only category, APHRODITE and GPCC perform better than CPC-uni and CRU data sets in terms of the spatial and temporal variations of skill matrices. Overall, GPCC shows the best performance (including the precipitation sensitivity to ENSO events) and is the preferable observational data set for long-term climatological analysis over the CSWA region. Among the satellite-derived precipitation products, 3B42-V7 displays the most realistic wintertime precipitation distribution pattern when compared to 3B42-V6 and GPCP-1DD; however, efforts are needed to further improve the accuracy of satellite-derived products over the dry arid and semi-arid areas of CSWA. In the reanalysis category, MERRA's performance is relatively better than CFSR and ERA-Interim, although significant biases exist in all of the reanalyses due to overestimation of precipitation over the mountainous regions of CSWA.