# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
###### Abstract
Satellite-based precipitation estimation products utilize satellite-enhanced meteorological observations, enabling estimation through the joint inversion of visible, infrared, microwave, or multi-sensor data [9; 10]. On the other hand, the reanalysis-based approach combines ground-based observation data with climate model forecasts using a data assimilation system to estimate surface precipitation [11; 12]. The choice of the Integrated Multi-Satellite Retrievals for GPM (IMERG) is justified by its ability to integrate multi-satellite sensors including passive microwave, infrared, and radar, its high spatiotemporal resolution, and its near real-time access. Also, it incorporates advanced data assimilation techniques that integrate satellite observations with other available data sources, such as gauge observations and numerical weather prediction models [13]. This assimilation process improves the accuracy and reliability of precipitation estimates. Furthermore, the strengths of the Fifth Generation of ECMWF Atmospheric Reanalysis (ERA5) lie in its long-term dataset and the use of rigorous quality control procedures to filter out erroneous or inconsistent observations. ERA5 also utilizes state-of-the-art numerical weather prediction models developed by ECMWF. These models integrate various atmospheric variables, including temperature, humidity, wind, and pressure, to generate consistent and physically based precipitation estimates [14]. These strengths make GPM and ERA5 a valuable resource for estimating and studying precipitation. Data from IMERG and ERA5 have been extensively examined and compared to observed data, both globally [7; 15; 16; 17; 18] and at the national scale as in sub-Saharan Morocco [19] or in the Oum Er Rbia watershed [20; 21]. These data have also been used for flood simulation [22; 23] and for climate change studies and drought monitoring in the Tensift sub-basin [24; 25]. However, studies evaluating the effectiveness of precipitation products in the Bouregreg watershed, in northwestern Morocco, are currently limited. The objective of this study is then to assess the reliability and accuracy of the IMERG V06 and ERA5 precipitation products for estimating precipitation in this region using machine learning metrics for classification and regression. Choosing the appropriate metric for evaluating a precipitation product's estimation capability is crucial. In a review conducted by the World Meteorological Organization [26], various evaluation scores were examined, including the correlation coefficient, the mean absolute error (MAE), the root mean square error (RMSE), the probability of detection (POD), and the false alarm rate (FAR). These parameters enable the measurement of both the precision and detection capability of the precipitation product [27]. This study considered these parameters, along with other machine learning metrics. Furthermore, although gridded precipitation products (GPPs) offer the advantages of global coverage and high spatiotemporal resolution [28], their use in hydrological applications is sometimes limited due to associated biases. These products are indeed susceptible to inherent errors, such as errors related to precipitation measurement [29], variations in land surface properties [30; 31], and variations in climate regimes, seasons, and elevations [32; 33; 34]. To address these concerns, this study proposes, in addition to the evaluation of gridded precipitation data, a framework to correct the biases of these precipitations.