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

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.

Deep Learning Techniques in Extreme Weather Events: A Review

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events.