A Comprehensive Evaluation of Near-Real-Time and Research Products of IMERG Precipitation over India for the Southwest Monsoon Period

Precipitation is one of the integral components of the global hydrological cycle. Accurate estimation of precipitation is vital for numerous applications ranging from hydrology to climatology. Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, the Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product was released. The IMERG provides global precipitation estimates at finer spatiotemporal resolution (e.g., 0.1°/half-hourly) and has shown to be better than other contemporary multi-satellite precipitation products over most parts of the globe. In this study, near-real-time and research products of IMERG have been extensively evaluated against a daily rain-gauge-based precipitation dataset over India for the southwest monsoon period. In addition, the current version 6 of the IMERG research product or Final Run (IMERG-F V6) has been compared with its predecessor, version 5, and error characteristics of IMERG-F V6 for pre-GPM and GPM periods have been assessed. The spatial distributions of different error metrics over the country show that both near-real-time IMERG products (e.g., Early and Late Runs) have similar error characteristics in precipitation estimation. However, near-real-time products have larger errors than IMERG-F V6, as expected. Bias in all-India daily mean rainfall in the near-real-time IMERG products is about 3–4 times larger than research product. Both V5 and V6 IMERG-F estimates show similar error characteristics in daily precipitation estimation over the country. Similarly, both near-real-time and research products show similar characteristics in the detection of rainy days. However, IMERG-F V6 exhibits better performance in precipitation estimation and detection of rainy days during the GPM period (2014–2017) than the pre-GPM period (2010–2013). The contribution of different rainfall intensity intervals to total monsoon rainfall is captured well by the IMERG estimates. Furthermore, results reveal that IMERG estimates under-detect and overestimate light rainfall intensity of 2.5–7.5 mm day−1, which needs to be improved in the next release. The results of this study would be beneficial for end-users to integrate this multi-satellite product in any specific application.

[1]  Vincenzo Levizzani,et al.  Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate , 2019, Remote. Sens..

[2]  Fei Yuan,et al.  Applications of TRMM- and GPM-Era Multiple-Satellite Precipitation Products for Flood Simulations at Sub-Daily Scales in a Sparsely Gauged Watershed in Myanmar , 2019, Remote. Sens..

[3]  Jian Zhou,et al.  Comprehensive evaluation of latest GPM era IMERG and GSMaP precipitation products over mainland China , 2020 .

[4]  J. Evans,et al.  How do different sensors impact IMERG precipitation estimates during hurricane days? , 2021, Remote Sensing of Environment.

[5]  Yaoyao Zheng,et al.  Assessment of the GPM and TRMM Precipitation Products Using the Rain Gauge Network over the Tibetan Plateau , 2018, Journal of Meteorological Research.

[6]  Daniel A. Vila,et al.  Precipitation Diurnal Cycle Assessment of Satellite-Based Estimates over Brazil , 2020, Remote. Sens..

[7]  Effect of offshore troughs on the South India erratic summer monsoon rainfall in June 2017 , 2021, Dynamics of Atmospheres and Oceans.

[8]  Mohamed A. Hamouda,et al.  Spatiotemporal evaluation of the GPM satellite precipitation products over the United Arab Emirates , 2019, Atmospheric Research.

[9]  O. P. Sreejith,et al.  Development of a new high spatial resolution (0.25° × 0.25°) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region , 2014 .

[10]  Che-Hao Chang,et al.  Evaluation of GPM-era Global Satellite Precipitation Products over Multiple Complex Terrain Regions , 2019, Remote. Sens..

[11]  Xiaoying Li,et al.  Evaluation of the GPM IMERG V06 products for light rain over Mainland China , 2021 .

[12]  D. S. Pai,et al.  A preliminary assessment of GPM-based multi-satellite precipitation estimates over a monsoon dominated region , 2018 .

[13]  Shailza Sharma,et al.  Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India , 2017, Scientific Reports.

[14]  Amir AghaKouchak,et al.  From TRMM to GPM: How well can heavy rainfall be detected from space? , 2016 .

[15]  Dalia Kirschbaum,et al.  The Global Precipitation Measurement (GPM) mission's scientific achievements and societal contributions: reviewing four years of advanced rain and snow observations , 2018, Quarterly journal of the Royal Meteorological Society. Royal Meteorological Society.

[16]  Vimal Mishra,et al.  Increase in extreme precipitation events under anthropogenic warming in India , 2018, Weather and Climate Extremes.

[17]  Aiwen Lin,et al.  Evaluation of the TRMM 3B42 and GPM IMERG products for extreme precipitation analysis over China , 2019, Atmospheric Research.

[18]  Yang Hong,et al.  Remote Sensing Precipitation: Sensors, Retrievals, Validations, and Applications , 2018 .

[19]  Marouane Temimi,et al.  Consistency of precipitation products over the Arabian Peninsula and interactions with soil moisture and water storage , 2018 .

[20]  D. S. Pai,et al.  Evaluation and inter-comparison of high-resolution multi-satellite rainfall products over India for the southwest monsoon period , 2019, International Journal of Remote Sensing.

[21]  B. Goswami,et al.  Role of interaction between dynamics, thermodynamics and cloud microphysics on summer monsoon precipitating clouds over the Myanmar Coast and the Western Ghats , 2014, Climate Dynamics.

[22]  Amir AghaKouchak,et al.  Error characterization of TRMM Multisatellite Precipitation Analysis (TMPA-3B42) products over India for different seasons , 2015 .

[23]  Sulochana Gadgil,et al.  Intense rainfall events over the west coast of India , 2006 .

[24]  Y. Hong,et al.  Two-decades of GPM IMERG early and final run products intercomparison: Similarity and difference in climatology, rates, and extremes , 2021 .

[25]  G. Huffman,et al.  The TRMM Multi-Satellite Precipitation Analysis (TMPA) , 2010 .

[26]  H. Kling,et al.  Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios , 2012 .

[27]  S. Gabriele,et al.  Assessment of GPM and SM2RAIN-ASCAT rainfall products over complex terrain in southern Italy , 2018, Atmospheric Research.

[28]  Soroosh Sorooshian,et al.  Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG) , 2020, Advances in Global Change Research.

[29]  Subimal Ghosh,et al.  Water-food-energy nexus with changing agricultural scenarios in India during recent decades , 2017 .

[30]  Chandranath Chatterjee,et al.  Does the GPM mission improve the systematic error component in satellite rainfall estimates over TRMM? An evaluation at a pan-India scale , 2016 .

[31]  Muhammad Azam,et al.  Assessment of IMERG-V06, TRMM-3B42V7, SM2RAIN-ASCAT, and PERSIANN-CDR Precipitation Products over the Hindu Kush Mountains of Pakistan, South Asia , 2020, Remote. Sens..

[32]  Adam Massmann,et al.  Assessment of GPM IMERG satellite precipitation estimation and its dependence on microphysical rain regimes over the mountains of south-central Chile , 2021 .

[33]  H. Sharif,et al.  Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda , 2021, Atmosphere.

[34]  Sulochana Gadgil,et al.  The Indian monsoon and its variability , 2003 .

[35]  Zhanqing Li,et al.  Diurnal variation and the influential factors of precipitation from surface and satellite measurements in Tibet , 2014 .

[36]  Marouane Temimi,et al.  Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters , 2020, Remote. Sens..