Assessment of GPM-IMERG and Other Precipitation Products against Gauge Data under Different Topographic and Climatic Conditions in Iran: Preliminary Results

Abstract

Abstract:The new generation of weather observatory satellites, namely Global Precipitation Measurement (GPM) constellation satellites, is the lead observatory of the 10 highly advanced earth orbiting weather research satellites. Indeed, GPM is the first satellite that has been designed to measure light rain and snowfall, in addition to heavy tropical rainfall. This work compares the final run of the Integrated Multi-satellitE Retrievals for GPM (IMERG) product, the post real time of TRMM and Multi-satellite Precipitation Analysis (TMPA-3B42) and the Era-Interim product from the European Centre for Medium Range Weather Forecasts (ECMWF) against the Iran Meteorological Organization (IMO) daily precipitation measured by the synoptic rain-gauges over four regions with different topography and climate conditions in Iran. Assessment is implemented for a one-year period from March 2014 to February 2015. Overall, in daily scale the results reveal that all three products lead to underestimation but IMERG performs better than other products and underestimates precipitation slightly in all four regions. Based on monthly and seasonal scale, in Guilan all products, in Bushehr and Kermanshah ERA-Interim and in Tehran IMERG and ERA-Interim tend to underestimate. The correlation coefficient between IMERG and the rain-gauge data in daily scale is far superior to that of Era-Interim and TMPA-3B42. On the basis of daily timescale of bias in comparison with the ground data, the IMERG product far outperforms ERA-Interim and 3B42 products. According to the categorical verification technique in this study, IMERG yields better results for detection of precipitation events on the basis of Probability of Detection (POD), Critical Success Index (CSI) and False Alarm Ratio (FAR) in those areas with stratiform and orographic precipitation, such as Tehran and Kermanshah, compared with other satellite/model data sets. In particular, for heavy precipitation (>15 mm/day), IMERG is superior to the other products in all study areas and could be used in future for meteorological and hydrological models, etc.

Multiscale Comparative Evaluation of the GPM IMERG v5 and TRMM 3B42 v7 Precipitation Products from 2015 to 2017 over a Climate Transition Area of China

“…The IMERG v5 product, with relatively high POD and CSI, showed better performance in precipitation detection of the Huaihe River basin. This was mainly because the GPM combined Instrument (GMI) sensor can capture light precipitation better than the TRMM combined Instrument (TMI) [42]. Additionally, Figure 9 demonstrated the FAR was increased, but the POD and CSI were reduced with increasing rainfall rate for the two satellite precipitation products, suggesting the limited capability of satellite sensors in detecting intense rainfall events [24].…”

Assessment of GPM and TRMM Multi-Satellite Precipitation Products in Streamflow Simulations in a Data-Sparse Mountainous Watershed in Myanmar

“…A few recent studies have proven that the GPM IMERG products are generally superior to TRMM in several regions, such as the Xinjiang region [30] and the Qinghai-Tibetan Plateau in China [30,31]; Mainland China [33,34]; Guilan, Bushehr, Kermanshah, and Tehran regions in Iran [37]; Far-East Asia [35]; and India [36]. However, in the present study, although the daily precipitation data of both the IMERG final run and TMPA 3B42V7 captured the spatiotemporal variation patterns of rainfall events in the Chindwin River basin in Myanmar, no significant improvements were found in IMERG.…”

“…Several previous studies [31][32][33][34][35][36][37] found that, although 3B42V7 and IMERG products effectively capture the spatiotemporal variations of precipitation in different regions around the world, these estimates still contain considerable errors when compared with ground observations. Given that precipitation inputs are among the most dominant uncertainty sources for hydrological models, satellite precipitation products must be bias-corrected when adopted as the input of a hydrological model for streamflow simulations.…”

Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

“…The probability of detection (POD) represents the ratio of the number of precipitation occurrences that are correctly detected by the satellite product (hits) to the number of precipitation occurrences that are observed by the reference data (hits plus misses), which is expressed as a/(a + c). The false alarm ratio (FAR) captures the number of rainfall events that were incorrectly detected, and it can be calculated by the equation b/(a + b) [17]. Table 3.…”