Merging Satellite-Based Precipitation Estimates and Rain Gauge Measurements over Saudi Arabia

The results of hydrological and climatic studies are influenced by the quantity of the accuracy of precipitations measurements. The rain gauges are the most trustful precipitation measurements at the observation point. However, the degree of the uncertainty increases when the rain gauges observations are extended to cover more spatial areas. In other hand, satellite-based precipitation estimations (SPEs) provides one additional source for the precipitations measurements because they can provide the precipitation measurements over a large area. Studies proved that the SPEs estimations are influenced by bias. In this study, a method that can improve the quality of precipitation estimations by merging between the rain gauges and SEPs is proposed. The method has two major steps. First, the systematic bias is removed by implementing a non-parametric quantile mapping and climatic zones approach to adjust satellite (PERSIANN-CCS) rainfall distribution (at 0.04^ox0.04^o lat-long scale). Seven years (2010 – 2016) of the daily precipitation data are used on this study. The data between 2010 and 2015 are used for the calibration, and 2016 year is used for validation. The results of the annual mean bias and root mean square error over the study area (Saudi Arabia) during the validation year are reduced by 89% and 73%, respectively. The second step combines between the gridded rain gauges and the bias adjustment of PERSIANN-CCS at (0.04°×0.04°) estimates. The rain gauges are gridded by implementing the inverse weighted distance to (0.04°×0.04° lat-long scale). The merged produced is calculated based on the uncertainty of the bias adjustment PERSIANN-CCS and gridded rain gauges. The results of the annual mean bias and root mean square error are reduced by 95% and 75% respectively. Based on the Statistical analysis of the merged product, the merging method can be implemented to remove the bias of SPEs.