Comparison of Spring Season Precipitation Derived from Four Gridded Datasets and Simulated by RegCM and Their Evaluation Against Observations in Ghazvin Plai

Precipitation is a major component of the hydrological cycle and is a crucial forcing data for hydrological and agricultural models. Great efforts have been made to generate gridded precipitation products, thereby leading to the increased availability of precipitation datasets at different spatial and temporal resolutions over the globe. This study provides an intercomparison of daily precipitation of four high spatial resolution gridded precipitation products (PERSIANN-CCS, CHIRPS, CFSR and AgMERRA) and that simulated by RegCM4 model and their evaluation against observational data over eight spring seasons (2003-2010) in the Qazvin Plain. Statistical evaluation of three convective precipitation schemes (Ku, Emanuel, and Grell) coupled in RegCm4 showed that Ku is the best scheme for spring precipitation modeling in the region. Based on continuous statistical indices, AgMERRA shows relative superiority to other products and RegCM. Categorical statisticsal indices indicated that CFSR performs better in detecting rainy days, but AgMERRA in detecting the non-rainy days and determining rainy and non-rainy days. Results showed that compared to other precipitation products, AgMERRA, in general, is closer to the observed daily precipitation during the spring over the Ghazvin Plain.