Evaluation and inter-comparison of high-resolution multi-satellite rainfall products over India for the southwest monsoon period. Issue 12 (18th June 2019)

• Record Type: Journal Article
• Authors: Reddy, M. Venkatarami; Mitra, Ashis K.; Momin, Imranali M.; Mitra, Ashim K.; Pai, D. S.
• Is Part Of: International journal of remote sensing. Volume 40:Issue 12(2019)
• Page Start: 4577
• Page End: 4603
• Publication Date: 2019-06-18
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2019.1569786
• Languages: English
• ISSNs: 0143-1161

Abstract

ABSTRACT: Rainfall is one of the key drivers of the global hydrological cycle and has large socio-economic impacts. Tropical rainfall accounts for two-thirds of the global rainfall and is primarily associated with the monsoon. Multi-satellite rainfall products provide rainfall with high temporal and spatial resolutions; however, they exhibit regional and seasonal biases. Evaluation of these products against ground-based observations can improve the accuracy of the estimated rainfall. With the launch of the Global Precipitation Measurement (GPM) Core Observatory, two advanced high-resolution multi-satellite precipitation products namely; Integrated Multi-satellite Retrievals for GPM (IMERG) and Global Satellite Mapping of Precipitation (GSMaP) are released. In the present study the spatial and temporal structures of rainfall in near real time and research versions of IMERG-V4 (near real-time (NRT) & Final (FNL)), GSMaP-V6 (NRT & moving vector with Kalman filter (MVK)), INSAT3D (Indian National Satellite System (INSAT) Multispectral Rainfall Algorithm (IMR) & Hydro-Estimator method (HEM)) and Indian Meteorological Department (IMD) – National Centre for Medium Range Weather Forecasting (NCMRWF) Merged product have been evaluated against gridded gauge-based IMD rainfall data on daily, monthly and seasonal scales. All the datasets show noticeable bias in producing rainfall over orographic regions (i.e. Western Ghats and foothills of Himalayas) and North-East India, though there exists significant difference among the satellite measurements. Different skill scores are computed for GSMaP, IMERG and INSAT3D data products to evaluate the performance of these satellite estimates. However in terms of biases IMD-NCMRWF Merged, GSMaP (NRT & MVK) and IMERG (NRT & FNL) underestimates rainfall (about 11%, 17%, 23%, 18% and 3%, respectively) and INSAT3D (IMR & HEM) overestimates (about 49% and 33%, respectively), for the India region as a whole. In a similar way, HEM product shows 15% better performance than IMR product in INSAT3D category. However, both NRT and MVK products of GSMaP show similar variations compared to observe rainfall. Overall IMD–NCMRWF merged and IMERG-FNL data products show better agreement with the gauge-based IMD data compared to GSMaP. The GPM-based products (IMERG and GSMaP) estimate rainfall much better than INSAT3D estimation.