Classification of tropical cyclone rain patterns using convolutional autoencoder
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 ye...
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ftpubmed:oai:pubmedcentral.nih.gov:10774432 2024-02-11T10:06:14+01:00 Classification of tropical cyclone rain patterns using convolutional autoencoder Kim, Dasol Matyas, Corene J. 2024-01-08 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10774432/ http://www.ncbi.nlm.nih.gov/pubmed/38191785 https://doi.org/10.1038/s41598-023-50994-5 en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10774432/ http://www.ncbi.nlm.nih.gov/pubmed/38191785 http://dx.doi.org/10.1038/s41598-023-50994-5 © The Author(s) 2024 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . Sci Rep Article Text 2024 ftpubmed https://doi.org/10.1038/s41598-023-50994-5 2024-01-14T01:57:23Z 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. Text North Atlantic PubMed Central (PMC) Scientific Reports 14 1 |
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Article Kim, Dasol Matyas, Corene J. Classification of tropical cyclone rain patterns using convolutional autoencoder |
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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 |
Text |
author |
Kim, Dasol Matyas, Corene J. |
author_facet |
Kim, Dasol Matyas, Corene J. |
author_sort |
Kim, Dasol |
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 Publishing Group UK |
publishDate |
2024 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10774432/ http://www.ncbi.nlm.nih.gov/pubmed/38191785 https://doi.org/10.1038/s41598-023-50994-5 |
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North Atlantic |
genre_facet |
North Atlantic |
op_source |
Sci Rep |
op_relation |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10774432/ http://www.ncbi.nlm.nih.gov/pubmed/38191785 http://dx.doi.org/10.1038/s41598-023-50994-5 |
op_rights |
© The Author(s) 2024 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
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https://doi.org/10.1038/s41598-023-50994-5 |
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