Anomaly-Aware Tropical Cyclone Track Prediction Using Multi-Scale Generative Adversarial Networks
Tropical cyclones (TCs) frequently encompass multiple hazards, including extreme winds, intense rainfall, storm surges, flooding, lightning, and tornadoes. Accurate methods for forecasting TC tracks are essential to mitigate the loss of life and property associated with these hazards. Despite signif...
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/4/583 |
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| author | He Huang Difei Deng Liang Hu Nan Sun |
| author_facet | He Huang Difei Deng Liang Hu Nan Sun |
| author_sort | He Huang |
| collection | DOAJ |
| description | Tropical cyclones (TCs) frequently encompass multiple hazards, including extreme winds, intense rainfall, storm surges, flooding, lightning, and tornadoes. Accurate methods for forecasting TC tracks are essential to mitigate the loss of life and property associated with these hazards. Despite significant advancements, accurately forecasting the paths of TCs remains a challenge, particularly when they interact with complex land features, weaken into remnants after landfall, or are influenced by abnormal satellite observations. To address these challenges, we propose a generative adversarial network (GAN) model with a multi-scale architecture that processes input data at four distinct resolution levels. The model is designed to handle diverse inputs, including satellite cloud imagery, vorticity, wind speed, and geopotential height, and it features an advanced center detection algorithm to ensure precise TC center identification. Our model demonstrates robustness during testing, accurately predicting TC paths over both ocean and land while also identifying weak TC remnants. Compared to other deep learning approaches, our method achieves superior detection accuracy with an average error of 41.0 km for all landfalling TCs in Australia from 2015 to 2020. Notably, for five TCs with abnormal satellite observations, our model maintains high accuracy with a prediction error of 35.2 km, which is a scenario often overlooked by other approaches. |
| format | Article |
| id | doaj-art-0a0974faa4e14f569c352d56d635c4db |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-0a0974faa4e14f569c352d56d635c4db2025-08-20T02:44:43ZengMDPI AGRemote Sensing2072-42922025-02-0117458310.3390/rs17040583Anomaly-Aware Tropical Cyclone Track Prediction Using Multi-Scale Generative Adversarial NetworksHe Huang0Difei Deng1Liang Hu2Nan Sun3School of Systems and Computing, University of New South Wales at Canberra, Canberra 2612, AustraliaSchool of Science, University of New South Wales at Canberra, Canberra 2612, AustraliaGeoscience Australia, Canberra 2609, AustraliaSchool of Systems and Computing, University of New South Wales at Canberra, Canberra 2612, AustraliaTropical cyclones (TCs) frequently encompass multiple hazards, including extreme winds, intense rainfall, storm surges, flooding, lightning, and tornadoes. Accurate methods for forecasting TC tracks are essential to mitigate the loss of life and property associated with these hazards. Despite significant advancements, accurately forecasting the paths of TCs remains a challenge, particularly when they interact with complex land features, weaken into remnants after landfall, or are influenced by abnormal satellite observations. To address these challenges, we propose a generative adversarial network (GAN) model with a multi-scale architecture that processes input data at four distinct resolution levels. The model is designed to handle diverse inputs, including satellite cloud imagery, vorticity, wind speed, and geopotential height, and it features an advanced center detection algorithm to ensure precise TC center identification. Our model demonstrates robustness during testing, accurately predicting TC paths over both ocean and land while also identifying weak TC remnants. Compared to other deep learning approaches, our method achieves superior detection accuracy with an average error of 41.0 km for all landfalling TCs in Australia from 2015 to 2020. Notably, for five TCs with abnormal satellite observations, our model maintains high accuracy with a prediction error of 35.2 km, which is a scenario often overlooked by other approaches.https://www.mdpi.com/2072-4292/17/4/583tropical cyclonetrack predictionGANsatellite image |
| spellingShingle | He Huang Difei Deng Liang Hu Nan Sun Anomaly-Aware Tropical Cyclone Track Prediction Using Multi-Scale Generative Adversarial Networks Remote Sensing tropical cyclone track prediction GAN satellite image |
| title | Anomaly-Aware Tropical Cyclone Track Prediction Using Multi-Scale Generative Adversarial Networks |
| title_full | Anomaly-Aware Tropical Cyclone Track Prediction Using Multi-Scale Generative Adversarial Networks |
| title_fullStr | Anomaly-Aware Tropical Cyclone Track Prediction Using Multi-Scale Generative Adversarial Networks |
| title_full_unstemmed | Anomaly-Aware Tropical Cyclone Track Prediction Using Multi-Scale Generative Adversarial Networks |
| title_short | Anomaly-Aware Tropical Cyclone Track Prediction Using Multi-Scale Generative Adversarial Networks |
| title_sort | anomaly aware tropical cyclone track prediction using multi scale generative adversarial networks |
| topic | tropical cyclone track prediction GAN satellite image |
| url | https://www.mdpi.com/2072-4292/17/4/583 |
| work_keys_str_mv | AT hehuang anomalyawaretropicalcyclonetrackpredictionusingmultiscalegenerativeadversarialnetworks AT difeideng anomalyawaretropicalcyclonetrackpredictionusingmultiscalegenerativeadversarialnetworks AT lianghu anomalyawaretropicalcyclonetrackpredictionusingmultiscalegenerativeadversarialnetworks AT nansun anomalyawaretropicalcyclonetrackpredictionusingmultiscalegenerativeadversarialnetworks |