Tropical Cyclone Forecast Using Multitask Deep Learning Framework

Wu, Yuqiao; Geng, Xiaoyi; Liu, Zili; Shi, Zhenwei

doi:10.1109/lgrs.2021.3132395

Cited by 10 publications

(3 citation statements)

References 15 publications

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“…The large-scale imagelike data with a high temporal resolution, such as radar map and satellite image, is the primary input of the proposed methods. Although the similar idea also investigates in TCs track forecast area recently [16], the model architecture and scale of the training set are still limited compared with the MetNet [32] and DGMR [33] which can be further studied. • The experiment results in Table VI shows the relatively bad performance for forecasting only based on TC features, especially for long-term forecast results (24h).…”

Section: Discussion and Future Workmentioning

confidence: 99%

“…M. Rüttgers et al used a Generative Adversarial Network (GAN) to predict the TCs track images and the corresponding location of TCs center [15]. Wu et al proposed a multitask machine learning framework based on an improved GAN to predict the track and intensity of TCs simultaneously [16]. The track forecast methods above take full advantage of the powerful performance of GANs in the field of computer vision.…”

Section: Introductionmentioning

confidence: 99%

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Dual-Branched Spatio-Temporal Fusion Network for Multihorizon Tropical Cyclone Track Forecast

Liu

Hao

Geng

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A tropical cyclone (TC) is a typical extreme tropical weather system, which could cause serious disasters in transit areas. Accurate TC track forecasting is the key to reducing casualties and damages, however, long-term forecasting of TCs is a challenging problem due to their extremely high dynamics and uncertainty. Existing TC track forecasting methods mainly focus on utilizing a single modality of source data, meanwhile, suffer from limited long-term forecasting capability and high computational complexity. In this paper, we propose to address the above challenges from a new perspective -by utilizing largescale spatio-temporal multimodal historical data and advanced deep learning techniques. A novel multi-horizon tropical cyclone track forecasting model named Dual-Branched spatio-temporal Fusion Network (DBF-Net) is proposed and evaluated. DBF-Net contains a TC features branch that extracts temporal features from 2D state vectors and a pressure field branch that extracts spatio-temporal features from reanalysis 3D pressure field. We show that with the above design, DBF-Net can fully exploit the implicit associations of multimodal data, achieving advantages that unimodal data-based method does not have. Extensive experiments on 39 years of historical TCs track data in the Northwest Pacific show that our DBF-Net achieves significant accuracy improvement compared with previous TCs track forecast methods.

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Section: Discussion and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dual-Branched Spatio-Temporal Fusion Network for Multihorizon Tropical Cyclone Track Forecast

Liu

Hao

Geng

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Utilizing a comprehensive dataset of intensity and trajectory information for tropical cyclones detected in the Western North Pacific since 1949, Pan et al [22] developed a recurrent neural network (RNN) approach to forecast tropical cyclone intensity. Wu et al [23] employed a modified generative adversarial network for predictive model design to make accurate forecasts of tropical cyclone spatial information. They leveraged two distinct deep neural networks in the estimation module, enabling them to glean placement and intensities from forecast-generated data.…”

Section: Literature Reviewmentioning

confidence: 99%

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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A Physics-informed Deep-learning Intensity Prediction Scheme for Tropical Cyclones over the Western North Pacific

Zhou,

Zhan,

Wang

et al. 2024

Adv. Atmos. Sci.

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Tropical Cyclone Forecast Using Multitask Deep Learning Framework

Cited by 10 publications

References 15 publications

Dual-Branched Spatio-Temporal Fusion Network for Multihorizon Tropical Cyclone Track Forecast

Dual-Branched Spatio-Temporal Fusion Network for Multihorizon Tropical Cyclone Track Forecast

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

A Physics-informed Deep-learning Intensity Prediction Scheme for Tropical Cyclones over the Western North Pacific

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