4.3 Article

Agreement between monthly precipitation estimates from TRMM satellite, NCEP reanalysis, and merged gauge-satellite analysis

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AMER GEOPHYSICAL UNION
DOI: 10.1029/2010JD015483

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  1. Research Agency for Climate Studies [RACS 2010-2013]
  2. National Research Foundation of Korea [과06A1403] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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Global monthly precipitation is a critical element in understanding variability of the Earth's climate including changes in the hydrological cycle associated with global warming. The NCEP reanalysis (R1), GPCP, CMAP, and TMPA precipitation data sets are often used in climate studies. This study compares the data sets (R1, GPCP, CMAP, and TMPA) with the TRMM precipitation data sets derived from the TRMM precipitation radar (TPR), microwave imager (TMI), and combined algorithm (TCA) for 11 years (1998-2008) over the satellite's domain (40 degrees S-40 degrees N). The domain precipitation estimates from seven data sets range from 2.44 to 3.38 mm d (1) over the ocean and from 1.98 to 2.83 mm d (1) over land. The regional differences between the TPR and the other data sets are analyzed by a paired t test. Particularly, statistically significant differences between TPR and GPCP and between TPR and CMAP are found in most oceanic regions and in some land areas. In general, there exists substantial disagreement in precipitation intensities from the precipitation data sets. Therefore, significant consideration is given to the uncertainties in the data sets prior to applying the results to climate studies such as estimations of the global hydrological budget analyses. Meanwhile, the anomalies from all the data sets agree relatively well in their variability patterns. It is also found that the dominant mode of interannual variability which is associated with the ENSO pattern is clearly demonstrated by all precipitation data sets. These results suggest that all considered precipitation data sets may produce similar results when they are used for climate variability analyses on annual to interannual time scales.

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