Improving short-term sea ice concentration forecasts using deep learning
Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physically based models still have limited accuracy, statistical post-processing techniques can be applied to reduce forecast errors. In this study, post-processing m...
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ftdoajarticles:oai:doaj.org/article:e46737514d8347b1b003c20fbfef6c0c 2024-09-15T18:34:06+00:00 Improving short-term sea ice concentration forecasts using deep learning C. Palerme T. Lavergne J. Rusin A. Melsom J. Brajard A. F. Kvanum A. Macdonald Sørensen L. Bertino M. Müller 2024-04-01T00:00:00Z https://doi.org/10.5194/tc-18-2161-2024 https://doaj.org/article/e46737514d8347b1b003c20fbfef6c0c EN eng Copernicus Publications https://tc.copernicus.org/articles/18/2161/2024/tc-18-2161-2024.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-18-2161-2024 1994-0416 1994-0424 https://doaj.org/article/e46737514d8347b1b003c20fbfef6c0c The Cryosphere, Vol 18, Pp 2161-2176 (2024) Environmental sciences GE1-350 Geology QE1-996.5 article 2024 ftdoajarticles https://doi.org/10.5194/tc-18-2161-2024 2024-08-05T17:49:28Z Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physically based models still have limited accuracy, statistical post-processing techniques can be applied to reduce forecast errors. In this study, post-processing methods based on supervised machine learning have been developed for improving the skill of sea ice concentration forecasts from the TOPAZ4 prediction system for lead times from 1 to 10 d . The deep learning models use predictors from TOPAZ4 sea ice forecasts, weather forecasts, and sea ice concentration observations. Predicting the sea ice concentration for the next 10 d takes about 4 min (including data preparation), which is reasonable in an operational context. On average, the forecasts from the deep learning models have a root mean square error 41 % lower than TOPAZ4 forecasts and 29 % lower than forecasts based on persistence of sea ice concentration observations. They also significantly improve the forecasts for the location of the ice edges, with similar improvements as for the root mean square error. Furthermore, the impact of different types of predictors (observations, sea ice, and weather forecasts) on the predictions has been evaluated. Sea ice observations are the most important type of predictors, and the weather forecasts have a much stronger impact on the predictions than sea ice forecasts. Article in Journal/Newspaper Sea ice The Cryosphere Directory of Open Access Journals: DOAJ Articles The Cryosphere 18 4 2161 2176 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
spellingShingle |
Environmental sciences GE1-350 Geology QE1-996.5 C. Palerme T. Lavergne J. Rusin A. Melsom J. Brajard A. F. Kvanum A. Macdonald Sørensen L. Bertino M. Müller Improving short-term sea ice concentration forecasts using deep learning |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
Reliable short-term sea ice forecasts are needed to support maritime operations in polar regions. While sea ice forecasts produced by physically based models still have limited accuracy, statistical post-processing techniques can be applied to reduce forecast errors. In this study, post-processing methods based on supervised machine learning have been developed for improving the skill of sea ice concentration forecasts from the TOPAZ4 prediction system for lead times from 1 to 10 d . The deep learning models use predictors from TOPAZ4 sea ice forecasts, weather forecasts, and sea ice concentration observations. Predicting the sea ice concentration for the next 10 d takes about 4 min (including data preparation), which is reasonable in an operational context. On average, the forecasts from the deep learning models have a root mean square error 41 % lower than TOPAZ4 forecasts and 29 % lower than forecasts based on persistence of sea ice concentration observations. They also significantly improve the forecasts for the location of the ice edges, with similar improvements as for the root mean square error. Furthermore, the impact of different types of predictors (observations, sea ice, and weather forecasts) on the predictions has been evaluated. Sea ice observations are the most important type of predictors, and the weather forecasts have a much stronger impact on the predictions than sea ice forecasts. |
format |
Article in Journal/Newspaper |
author |
C. Palerme T. Lavergne J. Rusin A. Melsom J. Brajard A. F. Kvanum A. Macdonald Sørensen L. Bertino M. Müller |
author_facet |
C. Palerme T. Lavergne J. Rusin A. Melsom J. Brajard A. F. Kvanum A. Macdonald Sørensen L. Bertino M. Müller |
author_sort |
C. Palerme |
title |
Improving short-term sea ice concentration forecasts using deep learning |
title_short |
Improving short-term sea ice concentration forecasts using deep learning |
title_full |
Improving short-term sea ice concentration forecasts using deep learning |
title_fullStr |
Improving short-term sea ice concentration forecasts using deep learning |
title_full_unstemmed |
Improving short-term sea ice concentration forecasts using deep learning |
title_sort |
improving short-term sea ice concentration forecasts using deep learning |
publisher |
Copernicus Publications |
publishDate |
2024 |
url |
https://doi.org/10.5194/tc-18-2161-2024 https://doaj.org/article/e46737514d8347b1b003c20fbfef6c0c |
genre |
Sea ice The Cryosphere |
genre_facet |
Sea ice The Cryosphere |
op_source |
The Cryosphere, Vol 18, Pp 2161-2176 (2024) |
op_relation |
https://tc.copernicus.org/articles/18/2161/2024/tc-18-2161-2024.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-18-2161-2024 1994-0416 1994-0424 https://doaj.org/article/e46737514d8347b1b003c20fbfef6c0c |
op_doi |
https://doi.org/10.5194/tc-18-2161-2024 |
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The Cryosphere |
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18 |
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4 |
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2161 |
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2176 |
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