Performance of the CMORPH and GPM IMERG Products over the United Arab Emirates

Abstract

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.

Development and Assessment of High-Resolution Radar-Based Precipitation Intensity-Duration-Curve (IDF) Curves for the State of Texas

“…The Q-Q plots of the modeled and observed AMS quantiles show that the Gumbel distribution underestimates the lower quantiles and overestimates the higher quantiles in all of the metropolitan areas (Figure 7). Many validation studies that were conducted to assess the remotely sensed precipitation observed that the products underestimate heavy precipitation and overestimate light to medium precipitation [20,[34][35][36], which is consistent with the results shown in Figure 7, with the exception of McAllen (Figure 7D). McAllen has the best fit and the lowest normalized root mean square error, while the worst fit was observed in Dallas-Fortworth, with a normalized root mean square error of 32%.…”

Impact of the Madden–Julian Oscillation on extreme precipitation over the western Maritime Continent and Southeast Asia

“…To our knowledge, IMERG has not yet been compared with the Climate Prediction Center MORPHing (CMORPH, Joyce et al ., 2004) dataset in the MC. Studies of IMERG and CMORPH in other regions show no clear evidence that one systematically outperforms the other in detecting extreme precipitation (Wei et al ., 2018; Lee et al ., 2019; Alsumaiti et al ., 2020; Xiao et al ., 2020). A recent study using nearly 20 years of gauge precipitation from Singapore, and taking into account their spatial sampling error, found that IMERG accurately represents extreme precipitation in this region (Mandapaka and Lo, 2020).…”

Physically-based, distributed hydrologic model for Makkah watershed using GPM satellite rainfall and ground rainfall stations

“…In general, the Early product reports the highest estimates while the Final product provides the lowest estimates. This is expected, as adjustment using climatological records would result in reduced estimates in dry regions (Alsumaiti et al 2020). The temporal patterns are generally similar.…”

“…The low spatial and temporal resolutions and low quality of precipitation data are considered to be major challenges in many regions across the globe, including the Arabian Peninsula (Hussein et al 2020). The high quality, highresolution global satellite precipitation products that recently became available can fill this gap and provide accurate input for many applications, including water resources planning and management, hydrologic modeling and forecasting, design of hydraulic structures, and flood, drought, and landslide analysis and forecasting (Sharif et al 2017;Alsumaiti et al 2020).…”