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Spatial assessment of the performance of multiple high‐resolution satellite‐based precipitation data sets over the Middle East
Author(s) -
El Kenawy Ahmed M.,
McCabe Matthew F.,
LopezMoreno Juan I.,
Hathal Yossef,
Robaa S. M.,
Al Budeiri Ahmed L.,
Jadoon Khan Zaib,
Abouelmagd Abdou,
Eddenjal Ali,
DomínguezCastro Fernando,
Trigo Ricardo M.,
VicenteSerrano Sergio M.
Publication year - 2019
Publication title -
international journal of climatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.58
H-Index - 166
eISSN - 1097-0088
pISSN - 0899-8418
DOI - 10.1002/joc.5968
Subject(s) - precipitation , climatology , environmental science , satellite , anomaly (physics) , correlation coefficient , meteorology , geography , statistics , geology , mathematics , physics , condensed matter physics , aerospace engineering , engineering
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).