Identification of tropical cyclone centre based on satellite images via deep learning techniques

Long, T.; Fu, Jiyang; Tong, Biao; Chan, P. W.; He, Yuncheng

doi:10.1002/joc.7909

Cited by 5 publications

(2 citation statements)

References 54 publications

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“…It consists of three gates and a memory cell state. The cell of LSTM is calculated as follows: it=σ(Wi.Y+bi) (3) ot=σ(Wo.Y+bo) (4) ct=(ftʘct-1)+itʘ tanh(Wc.Y+bc) (5) ht=otʘtanh(ct) (6) In the above equations it, ft, and ot represents the input, forget, and output gate, respectively, ct defines the cell state. The weighted matrices Wi, Wf, Wo∈R dx2d are mapped with hidden layer input with three gates and cell input.…”

Section: Model Developmentmentioning

confidence: 99%

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Enhancing Few-Shot Learning for Tropical Cyclone Severity Prediction: A Deep Learning Approach

Patil,

Bhosale

2023

MMEP

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The accurate prediction of tropical cyclone severity is of paramount importance in mitigating the potential damages arising from such catastrophic events. Constant monitoring and precise forecasting of tropical cyclones using remote satellite imagery from the Meteorological and Oceanographic Satellite Data Archival Centre (MOSDAC) are crucial. However, the challenge encountered with the current deep learning approach to image classification is its reliance on extensive labelled data and its limitations in few-shot learning. This study proposes a novel few-shot learning (FSL) approach for the prediction of tropical cyclone severity. In conjunction with FSL, the earth mover's distance (EMD) metric is employed to compute the distance between dense regions, thereby determining the relevance of an image. The methodology harnesses a remote satellite dataset provided by MOSDAC. The proposed approach is underpinned by the human capacity to identify novel classes from a limited number of samples, leveraging previously acquired knowledge. The FSL methodology adopts a meta-learning mechanism, enabling enhanced understanding of the data and facilitating the generalization of a new class of data. The results indicate that the FSL+EMD-based models outperform other state-of-the-art models, achieving a prediction accuracy of 85.8% in forecasting tropical cyclone severity from remote satellite imagery.

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Section: Model Developmentmentioning

confidence: 99%

“…Determining a tropical cyclone's intensity and rate of growth or decay necessitates repeated measurements. Despite these advanced techniques, satellite observation-based cyclone intensity level prediction remains a demanding task [6].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Few-Shot Learning for Tropical Cyclone Severity Prediction: A Deep Learning Approach

Patil,

Bhosale

2023

MMEP

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Short-term prediction of tropical cyclone track and intensity via four mainstream deep learning techniques

Gan,

Fu,

Zhao

et al. 2024

Journal of Wind Engineering and Industrial Aerodynamics

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Enhanced Typhoon Center Localization Using Geostationary Satellite Imagery

Zhou,

Min,

et al. 2024

Geophysical Research Letters

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An accurate center localization in near real‐time is critical for tropical cyclone (TC) monitoring and forecasting. This study presents a robust algorithm for localizing typhoon centers using the Chinese geostationary (GEO) meteorological satellite. The results using the Advanced Geostationary Radiation Imager (AGRI) onboard Fengyun‐4A (FY‐4A) satellite data, achieving a mean absolute error (MAE) of 29.4 km across various typhoon intensities in the Western North Pacific, superior to other baseline methods. By harnessing the multi‐spectral imagery from the FY‐4A and incorporating an attention mechanism, it significantly boosts the deep learning convolutional neural network's ability to identify typhoon cloud features and their centers, even during their initial and weakest stages, which is laudable because these are the most difficult for center fixing even for human analysts. Remarkably, it requires just a single moment satellite imagery to locate the center of typhoon, enabling automated updates of the typhoon centers in near real‐time applications.

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Identification of tropical cyclone centre based on satellite images via deep learning techniques

Cited by 5 publications

References 54 publications

Enhancing Few-Shot Learning for Tropical Cyclone Severity Prediction: A Deep Learning Approach

Enhancing Few-Shot Learning for Tropical Cyclone Severity Prediction: A Deep Learning Approach

Short-term prediction of tropical cyclone track and intensity via four mainstream deep learning techniques

Enhanced Typhoon Center Localization Using Geostationary Satellite Imagery

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