A review of the development and implementation of a tropical cyclone  prediction system for North Indian Ocean in a  multi-model ensemble framework

Ganesh, S. Saranya; Abhilash, S.; Joseph, Susmitha; Dey, Avijit; Mandal, Raju; Phani, R.; Chattopadhyay, Rajib; Pattanaik, D. R.; Sahai, A. K.; Kaur, Manpreet

doi:10.54302/mausam.v72i1.126

Cited by 4 publications

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“…Deshpande et al 28 also reported that, an increase in column averaged (950–150 ) MSE, which is a reflection of the thermal instability, that is playing a dominant role in the observed increase in TC frequency in AS. Following 49 , we have considered mean between 1000 and as a measure of thermal instability since at 1000 hPa include the effect of SST and heat fluxes and the mid-tropospheric include the effect of latent heat release associated with conditional instabilities and feedbacks during the storm intensification.…”

Section: Resultsmentioning

confidence: 99%

Changes in the thermodynamical profiles of the subsurface ocean and atmosphere induce cyclones to congregate over the Eastern Arabian Sea

Abhiram Nirmal,

Abhilash,

Martin

et al. 2023

Sci Rep

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The Arabian Sea accounts for a small fraction of Tropical Cyclones—about 2% of the annual global mean. However, the damage they might inflict there and along its coastlines, which are thickly populated, is considerable. This study explores the influence of the changes in the vertical profiles of atmosphere and oceanic environment throughout the seasons of March–June (MAMJ) and October–December (OND) in clustering the cyclogenesis over the Eastern Arabian Sea (EAS) next to the Indian West coast in recent decades. Further investigation has been done into the precise contribution of atmospheric and oceanic factors to fluctuations in cyclone intensity throughout the MAMJ and OND seasons separately. Two seasons have been studied independently in order to better understand the distinct influences of the vertical fluctuation of atmospheric factors and the thermal structure of the oceanic subsurface on cyclogenesis. More severe cyclones are caused by high tropical cyclone heat potential, and ocean subsurface warming present in this sea region influences the genesis of storms mostly during MAMJ. On the other hand, mid tropospheric relative humidity and thermal instability influences more on increasing cyclogenesis and its clustering over EAS during OND season. The findings suggest that large-scale oceanic subsurface conditions have a crucial influence on cyclogenesis over EAS through oceanic sensitivity to atmospheric forcing. This cyclone tendency and its clustering over EAS needs attention in terms of forecasting, catastrophe risk reduction, and climate change adaptation due to the security of coastal urban and rural habitats, livelihoods, and essential infrastructure along the coasts.

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Section: Resultsmentioning

confidence: 99%

Changes in the thermodynamical profiles of the subsurface ocean and atmosphere induce cyclones to congregate over the Eastern Arabian Sea

Abhiram Nirmal,

Abhilash,

Martin

et al. 2023

Sci Rep

Self Cite

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“…Specifically, Fast R-CNN employs a method known as selective search. To boost efficiency, Faster R-CNN substitutes the selective search process with the RPN (Regional Proposal Network), a comprehensive convolutional neural network [2]. This article will also shed light on a proposed modification of R-CNN, emphasizing its potential in vehicle tracking and speculating its applicability in human tracking.…”

Section: Introductionmentioning

confidence: 99%

Evolution of CNNs in human tracking applications

Chen

2024

ACE

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Tracking the movement of objects in videos or footage from CCTV systems plays an integral role in crime investigations, surveillance, security predictions, and many other domains. Historically, this task was primarily entrusted to dedicated observers or analysts who would be summoned to review pre-recorded footage post-event. With the advent of machine learning and AI, convolutional neural networks (CNNs) have paved the way for computers to augment human capabilities in analyzing streaming videos or archived recordings. Among the various tracking methodologies that machine learning offers, YOLO (You Only Look Once) and R-CNN (Region-based Convolutional Neural Networks), along with its iterations, stand out as some of the most reliable and precise. However, the scope of analysis often extends beyond these techniques. To enhance accuracy and provide an adept classification mechanism, the Deep SORT (Simple Online and Realtime Tracking) algorithm emerges as pivotal. Its synergy with human detection remains a significant area of discussion and will be deliberated upon in this study. This review aims to elucidate the intricacies of these state-of-the-art methods and their interplay in modern tracking systems.

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