A Long-term Global Comparison of IMERG and CFSR with Surface Precipitation Stations

Abstract For data sparse conditions of developing countries, gridded meteorological products have potential for different hydro-climatic applications. Comparisons of IMERG precipitation, at 0.1o, have been done with TRMM precipitation at station-, basin- and country-scale, in recent past. As IMERG products are expected to be available at least until mid-2030, a long-term global comparison of IMERG precipitation at station-scale, to guide their potential use, is highly desired. Therefore, we access surface precipitation stations from NOAA and compare their GSOM with IMERG at 0.1° during 2001–2020. Thus, we evaluate mean IMERG, CFSR and GSOM monthly precipitation with standard metrices like NSE, VE, KGE, R, RMSE and PBIAS for 5 geographical regions, 7 continents, 105 countries and > 50,000 surface locations. After comparison, we observe highest median NSE for Tropic of Capricorn (IMERG: CFSR = 0.85:0.59), followed by Antarctic (0.76:0.49), Arctic (0.71:0.32), Tropic of Cancer (0.64:0.14) and Frigid circle (0.47: -0.87). It shows satisfactory and unsatisfactory performances in the ‘fourth/first’ and ‘last/remaining regions’ for ‘IMERG/CFSR’, respectively. Fractional similarity of unit precipitation was good in Europe (VE = 0.72), North America (0.72), Asia (0.71), Australia (0.70) and satisfactory in South America (0.66) and Africa (0.52) for IMERG. Whereas, we find satisfactorily simulation for Europe (VE = 0.61), North America (0.56) and Australia (0.56), while other continents have unsatisfactory simulation of precipitation by CFSR. At country levels, 64 countries reveal a significantly better mean NSE with IMERG. While all these analyses pointed that IMERG monthly precipitation has better utility than CFSR in different hydro-meteorological applications, its site-specific application will still need detailed analysis at daily (sub-daily) resolutions. The outcomes of the study are expected to guide water resources managers to use these datasets in sustainable water resources management.