A Novel Deep Learning Model by BiGRU with Attention Mechanism for Tropical Cyclone Track Prediction in the Northwest Pacific

Song, Tao; Fan, Meng; Xie, Pengfei; Xu, Danya

doi:10.1175/jamc-d-20-0291.1

Cited by 28 publications

(12 citation statements)

References 18 publications

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“…This method generated a set of analogue forecasts by scanning an HWRF library for previous forecasts with similar significant characteristics to the latest HWRF estimates. Song et al [21] introduced a new deep learning (DL) method for predicting the path of tropical cyclones. This method utilized a bidirectional gated recurrent unit network (BiGRU) in conjunction with a specially designed attention function for path forecasting.…”

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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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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“…In recent years, deep learning (DL) has made rapid progress and has been successfully applied in various fields. Deep learning methods have shown prominent advantages over traditional methods in hydrological and meteorological applications, including runoff forecasting [20], precipitation nowcasting [21][22][23], quantitative precipitation estimation [24,25], cloud-type classification [26], tropical cyclone tracking [27], etc. Several researchers have made attempts to apply deep learning models in radar missing data completion.…”

Section: Introductionmentioning

confidence: 99%

Improving the Completion of Weather Radar Missing Data with Deep Learning

Gong,

Chen,

2023

Remote Sensing

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Weather radars commonly suffer from the data-missing problem that limits their data quality and applications. Traditional methods for the completion of weather radar missing data, which are based on radar physics and statistics, have shown defects in various aspects. Several deep learning (DL) models have been designed and applied to weather radar completion tasks but have been limited by low accuracy. This study proposes a dilated and self-attentional UNet (DSA-UNet) model to improve the completion of weather radar missing data. The model is trained and evaluated on a radar dataset built with random sector masking from the Yizhuang radar observations during the warm seasons from 2017 to 2019, which is further analyzed with two cases from the dataset. The performance of the DSA-UNet model is compared to two traditional statistical methods and a DL model. The evaluation methods consist of three quantitative metrics and three diagrams. The results show that the DL models can produce less biased and more accurate radar reflectivity values for data-missing areas than traditional statistical methods. Compared to the other DL model, the DSA-UNet model can not only produce a completion closer to the observation, especially for extreme values, but also improve the detection and reconstruction of local-scale radar echo patterns. Our study provides an effective solution for improving the completion of weather radar missing data, which is indispensable in radar quantitative applications.

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“…Artificial intelligence (AI) techniques have recently made significant advances in modeling and forecasting in the earth sciences [18]. AI has also made significant progress in TC forecasting, and the long-term forecasting of cyclone path has surpassed the performance of forecast centers [49]. In addition, the skill of TC rapid intensification (RI) by machine learning exceeds the NHC operational RI consensus [50].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic forecasting of tropical cyclones intensity using machine learning model

Meng

Yao

Wang

et al. 2023

Environ. Res. Lett.

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This study proposes a machine learning approach to probabilistic forecasting of tropical cyclone (TC) intensity. The earth system is complex and nonlinear, leading to inherent uncertainty in TC forecasting at all times, and therefore a representation of this uncertainty should be provided. Previous studies construct this uncertainty through ensemble or statistical methods, neither of which can directly characterize this uncertainty and suffer from problems such as excessive computational effort. And for this reason, we propose to assess the forecast without this uncertainty through the forecast distribution. Meanwhile, none of the previous studies on TC intensity forecasting by artificial intelligence(AI) methods characterize the uncertainty, so this study is a new supplement to data-driven TC forecasting. During the 2010-2020 evaluation period, the model’s point forecast can outperform the current state-of-the- art operational statistic-dynamical model resultsand can obtain forecast intervals to provide reliable probabilistic forecasts, which are critical for disaster warnings.

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A Novel Deep Learning Model by BiGRU with Attention Mechanism for Tropical Cyclone Track Prediction in the Northwest Pacific

Cited by 28 publications

References 18 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

Improving the Completion of Weather Radar Missing Data with Deep Learning

Probabilistic forecasting of tropical cyclones intensity using machine learning model

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