Evaluating Satellite Precipitation Products in Capturing the Spatio-temporal Rainfall Variability Across North Darfur State, Sudan

Abstract Accurate rainfall measurement is vital when investigating spatio-temporal precipitation variability, especially in arid lands. However, there are regions worldwide where only a few ground-based observations are made. This research evaluated the applicability of six satellite precipitation products (SPPs) in detecting rainfall variability in North Darfur State, Sudan. The SPPs were Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), African Rainfall Climatology (ARC), Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), Integrated Multi-satellitE Retrievals for Global Precipitation Measurements (GPM) Final Run (GPMIMERG), Precipitation Estimation from Remote Sensing Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT). The SPPs were assessed at daily, monthly, and annual timescales for 2000–2019. Four categorical indices, i.e., the probability of detection (POD), probability of false alarm (POFA), bias in detection (BID) and Heidke skill score (HSS), and four continuous indices, i.e., the Pearson correlation coefficient ( r ), the root mean square error (RMSE), the per cent bias (Pbias), and the Nash–Sutcliffe model efficiency coefficient (NSE) were used to evaluate the accuracy of the SPPs. Results of the statistical analysis showed that (1) at the daily timescale, the SPPs underestimate daily rainfall by 6.53–17.61%, and CHIRPS was the best for detecting rainy days, while PERSIANN-CDR performed poorly; (2) monthly and annual scales performed better than daily timescale, and TAMSAT and CHIRPS portrayed better performance than the other SPPs. Therefore, the two could reasonably estimate rainfall amounts in North Darfur State.