Estimation of the Area and Precipitation Associated with a Tropical Cyclone Biparjoy by using Image Processing

TL;DR

The paper addresses the challenge of quantifying rainfall from tropical cyclones (TCs) by developing an image-processing workflow applied to IMERG satellite data to identify cyclonic precipitation clusters and estimate both rainfall and affected area. The method processes IMERG Late products through re-projection, ROI masking, and connected-component analysis, aligning detected clusters with the Biparjoy best track to derive daily rainfall and spatial extent. The study provides quantitative metrics for Biparjoy across the Arabian Sea and India, including a daily average rainfall of $53.14$ mm/day over the region and a total affected area of $411.76\times 10^{3}$ km$^2$, with state-level impacts highlighted (e.g., Gujarat, Rajasthan, MP, UP). These results demonstrate the feasibility of satellite-based TC rainfall estimation and offer data-driven inputs to improve disaster readiness and region-specific response planning under changing climate conditions.

Abstract

The rainfall associated with Topical Cyclone(TC) contributes a major amount to the annual rainfall in India. Due to the limited research on the quantitative precipitation associated with Tropical Cyclones (TC), the prediction of the amount of precipitation and area that it may cover remains a challenge. This paper proposes an approach to estimate the accumulated precipitation and impact on affected area using Remote Sensing data. For this study, an instance of Extremely Severe Cyclonic Storm, Biparjoy that formed over the Arabian Sea and hit India in 2023 is considered in which we have used the satellite images of IMERG-Late Run of Global Precipitation Measurement (GPM). Image processing techniques were employed to identify and extract precipitation clusters linked to the cyclone. The results indicate that Biparjoy contributed a daily average rainfall of 53.14 mm/day across India and the Arabian Sea, with the Indian boundary receiving 11.59 mm/day, covering an extensive 411.76 thousand square kilometers. The localized intensity and variability observed in states like Gujarat, Rajasthan, Madhya Pradesh, and Uttar Pradesh highlight the need for tailored response measures, emphasizing the importance of further research to enhance predictive models and disaster readiness, crucial for building resilience against the diverse impacts of tropical cyclones.

Figures (12)

• Figure 1: Study area
• Figure 2: Workflow of the proposed approach
• Figure 3: Pre-processed images for the D_ID (a)-(n): D1-D14
• Figure 4: Cloud mask for D_ID (a)-(n): D1-D14
• Figure 5: Best Track of Biprajoy Cyclone