Classification of tropical cyclone rain patterns using convolutional autoencoder
Abstract Heavy rainfall produced by tropical cyclones (TCs) frequently causes wide-spread damage. TCs have different patterns of rain depending on their development stage, geographical location, and surrounding environmental conditions. However, an objective system for classifying TC rain patterns h...
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ftdoajarticles:oai:doaj.org/article:92544dfa63ad43ce89c39e3914edcd14 2024-02-11T10:06:15+01:00 Classification of tropical cyclone rain patterns using convolutional autoencoder Dasol Kim Corene J. Matyas 2024-01-01T00:00:00Z https://doi.org/10.1038/s41598-023-50994-5 https://doaj.org/article/92544dfa63ad43ce89c39e3914edcd14 EN eng Nature Portfolio https://doi.org/10.1038/s41598-023-50994-5 https://doaj.org/toc/2045-2322 doi:10.1038/s41598-023-50994-5 2045-2322 https://doaj.org/article/92544dfa63ad43ce89c39e3914edcd14 Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024) Medicine R Science Q article 2024 ftdoajarticles https://doi.org/10.1038/s41598-023-50994-5 2024-01-21T01:43:09Z Abstract Heavy rainfall produced by tropical cyclones (TCs) frequently causes wide-spread damage. TCs have different patterns of rain depending on their development stage, geographical location, and surrounding environmental conditions. However, an objective system for classifying TC rain patterns has not yet been established. This study objectively classifies rain patterns of North Atlantic TCs using a Convolutional Autoencoder (CAE). The CAE is trained with 11,991 images of TC rain rates obtained from satellite precipitation estimates during 2000−2020. The CAE consists of an encoder which compresses the original TC rain image into low-dimensional features and a decoder which reconstructs an image from the compressed features. Then, TC rain images are classified by applying a k-means method to the compressed features from the CAE. We identified six TC rain patterns over the North Atlantic and confirmed that they exhibited unique characteristics in their spatial patterns (e.g., area, asymmetry, dispersion) and geographical locations. Furthermore, the characteristics of rain patterns in each cluster were closely related to storm intensity and surrounding environmental conditions of moisture supply, vertical wind shear, and land interaction. This classification of TC rain patterns and further investigations into their evolution and spatial variability can improve forecasts and help mitigate damage from these systems. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles Scientific Reports 14 1 |
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Medicine R Science Q |
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Medicine R Science Q Dasol Kim Corene J. Matyas Classification of tropical cyclone rain patterns using convolutional autoencoder |
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Medicine R Science Q |
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Abstract Heavy rainfall produced by tropical cyclones (TCs) frequently causes wide-spread damage. TCs have different patterns of rain depending on their development stage, geographical location, and surrounding environmental conditions. However, an objective system for classifying TC rain patterns has not yet been established. This study objectively classifies rain patterns of North Atlantic TCs using a Convolutional Autoencoder (CAE). The CAE is trained with 11,991 images of TC rain rates obtained from satellite precipitation estimates during 2000−2020. The CAE consists of an encoder which compresses the original TC rain image into low-dimensional features and a decoder which reconstructs an image from the compressed features. Then, TC rain images are classified by applying a k-means method to the compressed features from the CAE. We identified six TC rain patterns over the North Atlantic and confirmed that they exhibited unique characteristics in their spatial patterns (e.g., area, asymmetry, dispersion) and geographical locations. Furthermore, the characteristics of rain patterns in each cluster were closely related to storm intensity and surrounding environmental conditions of moisture supply, vertical wind shear, and land interaction. This classification of TC rain patterns and further investigations into their evolution and spatial variability can improve forecasts and help mitigate damage from these systems. |
format |
Article in Journal/Newspaper |
author |
Dasol Kim Corene J. Matyas |
author_facet |
Dasol Kim Corene J. Matyas |
author_sort |
Dasol Kim |
title |
Classification of tropical cyclone rain patterns using convolutional autoencoder |
title_short |
Classification of tropical cyclone rain patterns using convolutional autoencoder |
title_full |
Classification of tropical cyclone rain patterns using convolutional autoencoder |
title_fullStr |
Classification of tropical cyclone rain patterns using convolutional autoencoder |
title_full_unstemmed |
Classification of tropical cyclone rain patterns using convolutional autoencoder |
title_sort |
classification of tropical cyclone rain patterns using convolutional autoencoder |
publisher |
Nature Portfolio |
publishDate |
2024 |
url |
https://doi.org/10.1038/s41598-023-50994-5 https://doaj.org/article/92544dfa63ad43ce89c39e3914edcd14 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024) |
op_relation |
https://doi.org/10.1038/s41598-023-50994-5 https://doaj.org/toc/2045-2322 doi:10.1038/s41598-023-50994-5 2045-2322 https://doaj.org/article/92544dfa63ad43ce89c39e3914edcd14 |
op_doi |
https://doi.org/10.1038/s41598-023-50994-5 |
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Scientific Reports |
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14 |
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1790603838710874112 |