This study presents the first comprehensive evaluation of the performance of three globally high-resolution remotely sensed products in replicating the main characteristics of rainfall over the Middle East, with special emphasis on extreme wet events. Specifically, we employed daily observational data from a network of rain gauges (N = 217), spanning the retrospective period 1998–2013 and covering six countries in the Middle East (i.e., Egypt, Iraq, Jordan, Libya, Saudi Arabia, and Syria), against data derived from three global satellite-based precipitation products: the Version 7 TRMM (Tropical Rainfall Measuring Mission) Multi-satellite Precipitation Analysis 3B42 product (TRMM-3B42), the Climate Prediction Center MORPHing technique (CMORPH), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Alongside a range of conventional statistical error measures (e.g., bias, normalized root-mean-square error [nRMSE] and Spearman's rho correlation coefficient), this study also gives priority to evaluate the skill of these products in reproducing characteristics of extreme wet events (e.g., frequency, intensity, duration, onset, anomaly). Results demonstrate that TRMM-3B42 generally performs well in estimating rainfall totals during the rainy season (ONDJFMA), with a mean bias of 0.05 mm, nRMSE of 0.15 mm, and Spearman's rho of 0.74 for the whole Middle East. In contrast, PERSIANN-CDR and CMORPH-BLD underestimate the observed rainfall. Importantly, TRMM-3B42 outperforms other products in reproducing the frequency and intensity of the most extreme wet events, while PERSIANN-CDR and CMORPH-BLD fail to reproduce these key characteristics. Notably, all products perform poorly in reproducing the climatology of the anomalous wet events in the region, indicating that careful scrutiny must be warranted before using these products, particularly for hydrological modelling. Considering the daily resolution of these remotely sensed precipitation products and their reasonable spatial resolution (0.25 × 0.25°) in comparison to available in situ data over the Middle East, results of this work provide a solid scientific reference for national stakeholders and policy makers to decide on the most useful product for their specific applications (e.g., hydrological modelling, streamflow forecasts, water resources management, and hydrometeorological hazard assessment and mitigation).
KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the research projects PCIN-2015-220 and CGL2014-52135-C03-01 financed by the Spanish Commission of Science and Technology and FEDER, IMDROFLOOD financed by the Water Works 2014 co-funded call of the European Commission and INDECIS, which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462). Ahmed El Kenawy is supported by Juan de la Cierva Fellowship. Matthew McCabe was funded by the King Abdullah University of Science and Technology. The authors are grateful to the Egyptian Meteorological Authority, the Libyan National Meteorological Centre, the Saudi Ministry of Environment, Water and Agriculture (MEWA), the Iraqi Meteorological Organization, the Syrian Meteorological Department and the Jordanian Ministry of Water and Irrigation for providing their daily rainfall data used in this study. The authors are grateful to two referees who have given valuable comments and suggestions on an earlier version of the manuscript. Their suggestions and comments have led to substantial improvements in this manuscript, both in content and style.