TY - JOUR
T1 - Performance of the CMORPH and GPM IMERG products over the United Arab Emirates
AU - Alsumaiti, Tareefa S.
AU - Hussein, Khalid
AU - Ghebreyesus, Dawit T.
AU - Sharif, Hatim O.
N1 - Publisher Copyright:
© 2020 by the authors.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - Satellite-based precipitation products are becoming available at very high temporal and spatial resolutions, which has accelerated their use in various hydro-meteorological and hydro-climatological applications. Because the quantitative accuracy of such products is affected by numerous factors related to atmospheric and terrain properties, validating them over different regions and environments is needed. This study investigated the performance of two high-resolution global satellite-based precipitation products: the climate prediction center MORPHing technique (CMORPH) and the latest version of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Mission (GPM) algorithm (IMERG), V06, over the United Arab Emirates from 2010 through 2018. The estimates of the products and that of 71 in situ rain gauges distributed across the country were compared by employing several common quantitative, categorical, and graphical statistical measures at daily, event-duration, and annual temporal scales, and at the station and study area spatial scales. Both products perform quite well in rainfall detection (above 70%), but report rainfall not observed by the rain gauges at an alarming rate (more than 30%), especially for light rain (lower quartile). However, for moderate and intense (upper quartiles) rainfall rates, performance is much better. Because both products are highly correlated with rain gauge observations (mostly above 0.7), the satellite rainfall estimates can probably be significantly improved by removing the bias. Overall, the CMORPH and IMERG estimates demonstrate great potential for filling spatial gaps in rainfall observations, in addition to improving the temporal resolution. However, further improvement is required, regarding the overestimation and underestimation of small and large rainfall amounts, respectively.
AB - Satellite-based precipitation products are becoming available at very high temporal and spatial resolutions, which has accelerated their use in various hydro-meteorological and hydro-climatological applications. Because the quantitative accuracy of such products is affected by numerous factors related to atmospheric and terrain properties, validating them over different regions and environments is needed. This study investigated the performance of two high-resolution global satellite-based precipitation products: the climate prediction center MORPHing technique (CMORPH) and the latest version of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Mission (GPM) algorithm (IMERG), V06, over the United Arab Emirates from 2010 through 2018. The estimates of the products and that of 71 in situ rain gauges distributed across the country were compared by employing several common quantitative, categorical, and graphical statistical measures at daily, event-duration, and annual temporal scales, and at the station and study area spatial scales. Both products perform quite well in rainfall detection (above 70%), but report rainfall not observed by the rain gauges at an alarming rate (more than 30%), especially for light rain (lower quartile). However, for moderate and intense (upper quartiles) rainfall rates, performance is much better. Because both products are highly correlated with rain gauge observations (mostly above 0.7), the satellite rainfall estimates can probably be significantly improved by removing the bias. Overall, the CMORPH and IMERG estimates demonstrate great potential for filling spatial gaps in rainfall observations, in addition to improving the temporal resolution. However, further improvement is required, regarding the overestimation and underestimation of small and large rainfall amounts, respectively.
KW - CMORPH
KW - GPM
KW - Precipitation
KW - United Arab Emirates
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U2 - 10.3390/RS12091426
DO - 10.3390/RS12091426
M3 - Article
AN - SCOPUS:85085285177
SN - 2072-4292
VL - 12
JO - Remote Sensing
JF - Remote Sensing
IS - 9
M1 - 1426
ER -