Assessment of INSAT-3D-derived high-resolution real-time precipitation products for North Indian Ocean cyclones

Satya Prakash () and S. C. Bhan
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Satya Prakash: India Meteorological Department, Ministry of Earth Sciences
S. C. Bhan: India Meteorological Department, Ministry of Earth Sciences

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 115, issue 1, No 38, 993-1009

Abstract: Abstract Accurate, high-resolution, real-time estimation of rainfall that is associated with tropical cyclones (TCs) is vital for disaster preparedness. In this study, three high-resolution, real-time precipitation products from the INSAT-3D geostationary satellite, namely INSAT multispectral rainfall (IMR), corrected IMR (IMC) and hydro-estimator method (HEM) are extensively assessed against a daily merged satellite-gauge rainfall product for 14 TCs in the North Indian Ocean (NIO) between 2019 and 2021. Results indicate that the INSAT-3D precipitation products exhibit an overall overestimation of TC rainfall as compared to the reference dataset, and the error characteristics in the INSAT-3D precipitation products do not show systematic relation with TC intensity. The error characteristics of the INSAT-3D-derived precipitation products show better performance over ocean than over land. However, IMR shows noticeably higher correlation coefficient and smaller root mean square error over land than IMC and HEM. Although HEM has negligible bias in TC rainfall estimation over the Arabian Sea, bias decomposition indicates anomalously large contributions of both positive and negative hit precipitation bias components. This analysis also suggests that a systematic bias correction to IMR and IMC may essentially improve TC rainfall estimates, but it may not be beneficial for HEM. Although IMC is an improved version of IMR, it does not show improvement over IMR in TC rainfall estimation. Furthermore, error characteristics of TC rainfall from the INSAT-3D precipitation products for different daily rainfall intensity categories show rather larger error and bias for light rainfall estimation. This study suggests that the INSAT-3D precipitation retrieval algorithms could be significantly benefited by the improvement in light rainfall estimation and by minimizing the false detection of very heavy rainfall events.

Keywords: Precipitation; INSAT-3D; Tropical cyclone; North Indian Ocean; Error characteristics; Bias decomposition (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-022-05582-7

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