Spatio-temporal evaluation of remote sensing rainfall data of TRMM satellite over the Kingdom of Saudi Arabia

Abstract

Rainfall estimation is the most important parameter for many water resource simulations and practices; therefore, precise and long-term data are required for trustworthy precipitation depiction. Recent advancements in remote sensing applications enabled researchers to estimate rainfall with greater geographical and temporal precision. The goal of this study was to evaluate the performance of a climatological satellite, the Tropical Rainfall Measuring Mission (TRMM) in estimating rainfall, with ground-based gauge data for five years (2008–2012) across the entire Kingdom of Saudi Arabia (KSA). In regional and station-based evaluations, many statistical performance metrics such as R-square (R2), root-mean-squared error (RMSE), mean absolute error (MAE), relative BIAS (R.B.), and correlation coefficient (CC) have been utilized. The southern, north-western, and south-western areas performed very well in the regression and correlation analyses. The problem of under and overestimating satellite data, according to R.B. analysis, exists across the Kingdom, with the southern, eastern, and north-western areas dominating (maximum over is R.B. = 94.6% and minimum over is 7.5%, while maximum under R.B. = − 52.8% and minimum under R.B. = − 5.9%). The RMSE and MAE were higher in the Qassim, Jazan, and Makkah regions, whereas they were the lowest in the northwestern. In general, TRMM prominently identified rainfall in comparison with the ground-based data and performed moderately for the majority of stations and regions during the research period.