Long‐Term High‐Resolution Gauge Adjusted Satellite Rainfall Product Over India. Issue 12 (19th December 2022)

• Record Type: Journal Article
• Authors: Kumar, Prashant; Varma, Atul K.; Kubota, Takuji; Yamaji, Moeka; Tashima, Tomoko; Mega, Tomoaki; Ushio, Tomoo
• Is Part Of: Earth and space science. Volume 9:Issue 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-19
• Subjects: rainfall -- gauges -- satellite -- validation -- precipitation radar; Space sciences -- Periodicals; Geophysics -- Periodicals; 500.5
• DOI: 10.1029/2022EA002595
• Languages: English
• ISSNs: 2333-5084

Abstract

Abstract: This study aims to create a 21‐year, high spatiotemporal resolution Global Satellite Mapping of Precipitation (GSMaP) rainfall product adjusted by rain gauge measurements over the Indian mainland and highlighted the importance of the Indian Meteorological Department (IMD) daily gridded rainfall to generate gauge adjusted GSMaP rainfall products over Indian landmass. The targeted resolutions of the GSMaP are hourly and 0.1° × 0.1°. The National Oceanic and Atmospheric Administration Climate Prediction Center daily gauge analysis (0.5° × 0.5°) and IMD daily gridded rainfall (0.25° × 0.25°) were utilized to generate long‐term rainfall products, GSMaP_CPC and GSMaP_IMD rainfall, respectively. After preliminary verification of the GSMaP_CPC and GSMaP_IMD rainfalls with IMD gauges, these rainfall products are evaluated for the Indian Summer Monsoon periods of 2000–2020 with comparisons of other gauge adjusted rainfall products such as the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement final‐run. The results suggest GSMaP_IMD has a smaller root‐mean‐square difference (RMSD) and higher correlation than GSMaP_CPC, evaluated against independent rainfall products. In the 3‐hour mean analysis with spaceborne precipitation radar data, it is found that the value of RMSD decreases in GSMaP_IMD with respect to GSMaP_CPC throughout the day. The statistics against the hourly dense gauge network suggests that the GSMaP_IMD is more effective in capturing large spatiotemporal rainfall variation. Thus, validation results with the independent sources suggest that GSMaP_IMD rainfall generally improved over GSMaP_CPC rainfall. These improvements are significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern parts of India. Key Points: Generate a long‐term high spatiotemporal resolution satellite rainfall product adjusted by rain gauge measurements over Indian mainland The new satellite rainfall product adjusted by gauges had a smaller error and higher correlation against independent sources These improvements were significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern India