UC Irvine

Accurate precipitation estimation at fine spatial and temporal scale is crucial for climatological studies. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) is a well-known estimation product and is bias-corrected using the Global Precipitation Climatology Project (GPCP), which has been recently updated to version 2.3. In this study, we compare the PERSIANN-CDR dataset that is bias-corrected with GPCP V2.3 (called PERSIANN-CDR V2.3) with the previous version, which was bias-corrected by GPCP V2.2 (PERSIANN-CDR V2.2), at monthly and daily scales. First, we discuss the changes between the two versions of PERSIANN-CDR using Mean Absolute Difference (MAD) and Relative Mean Absolute Difference (MARD) at the monthly scales over the globe. The results show noticeable differences between PERSIANN-CDR V2.3 & V2.2 over the ocean for latitudes from 40 to 60 after 2003. The changes are also significant over the land area from 2009 onward. Second, we evaluate the improvements in the new version of PERSIANN-CDR (V2.3) with respect to a gauged-based reference, data from Climate Prediction Center (CPC), at monthly and daily scales over all globe land areas and again over CONUS. Over the globe, the estimation of PERSIANN-CDR V2.3 is more accurate than PERSIANN-CDR V2.2, especially over CONUS and Australia. Over CONUS, Root Mean Square Error (RMSE) has decreased by 4.3 % and Correlation Coefficient (CC) has improved by 3.8 % compared to PERSIANN-CDR V2.2. The results emphasize that PERSIANN-CDR V2.3 has significantly improved in performance owing to refinement and input data from GPCP beginning from 2003.