Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application
In this study, the potential for sea ice concentration prediction using machine‐learning methods is investigated. Three different sea ice prediction models are compared: one high‐resolution dynamical assimilative model and two statistical machine‐learning models. The properties of all three models a...
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ftoslouniv:oai:www.duo.uio.no:10852/80863 2023-05-15T18:16:15+02:00 Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application Fritzner, Sindre Markus Graversen, Rune Christensen, Kai Håkon 2020-10-30T10:04:58Z http://hdl.handle.net/10852/80863 http://urn.nb.no/URN:NBN:no-83947 https://doi.org/10.1029/2020JC016277 EN eng http://urn.nb.no/URN:NBN:no-83947 Fritzner, Sindre Markus Graversen, Rune Christensen, Kai Håkon . Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application. Journal of Geophysical Research (JGR): Oceans. 2020, 125(11), 1-23 http://hdl.handle.net/10852/80863 1843517 info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal of Geophysical Research (JGR): Oceans&rft.volume=125&rft.spage=1&rft.date=2020 Journal of Geophysical Research (JGR): Oceans 125 11 1 23 https://doi.org/10.1029/2020JC016277 URN:NBN:no-83947 Fulltext https://www.duo.uio.no/bitstream/handle/10852/80863/2/2020JC016277.pdf 2169-9275 Journal article Tidsskriftartikkel Peer reviewed PublishedVersion 2020 ftoslouniv https://doi.org/10.1029/2020JC016277 2021-04-21T22:30:56Z In this study, the potential for sea ice concentration prediction using machine‐learning methods is investigated. Three different sea ice prediction models are compared: one high‐resolution dynamical assimilative model and two statistical machine‐learning models. The properties of all three models are explored, and the quality of their forecasts is compared. The dynamical model is a state‐of‐the‐art coupled ocean and sea ice ensemble‐prediction system with assimilation. The observations assimilated are high‐resolution sea ice concentration from synthetic aperture radar (SAR) and sea surface temperature from infrared instruments. The machine‐learning prediction models are a fully convolutional network and a k‐nearest neighbors method. These methods use several variables as input for the prediction: sea ice concentration, sea surface temperature, and 2‐m air temperature. Earlier studies have applied machine‐learning approaches primarily for seasonal ice forecast. Here we focus on short‐term predictions with a length of 1–4 weeks, which are of high interest for marine operations. The goal is to predict the future state of the sea ice using the same categories as traditional ice charts. The machine‐learning forecasts were compared to persistence, which is the assumption that the sea ice does not change over the forecasting period. The machine‐learning forecasts were found to improve upon persistence in periods of substantial change. In addition, compared to the dynamical model, the k‐nearest neighbor algorithm was found to improve upon the 7‐day forecast during a period of small sea ice variations. The fully convolutional network provided similar quality as the dynamical forecast. The study shows that there is a potential for sea ice predictions using machine‐learning methods. Article in Journal/Newspaper Sea ice Universitet i Oslo: Digitale utgivelser ved UiO (DUO) Journal of Geophysical Research: Oceans 125 11 |
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Universitet i Oslo: Digitale utgivelser ved UiO (DUO) |
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language |
English |
description |
In this study, the potential for sea ice concentration prediction using machine‐learning methods is investigated. Three different sea ice prediction models are compared: one high‐resolution dynamical assimilative model and two statistical machine‐learning models. The properties of all three models are explored, and the quality of their forecasts is compared. The dynamical model is a state‐of‐the‐art coupled ocean and sea ice ensemble‐prediction system with assimilation. The observations assimilated are high‐resolution sea ice concentration from synthetic aperture radar (SAR) and sea surface temperature from infrared instruments. The machine‐learning prediction models are a fully convolutional network and a k‐nearest neighbors method. These methods use several variables as input for the prediction: sea ice concentration, sea surface temperature, and 2‐m air temperature. Earlier studies have applied machine‐learning approaches primarily for seasonal ice forecast. Here we focus on short‐term predictions with a length of 1–4 weeks, which are of high interest for marine operations. The goal is to predict the future state of the sea ice using the same categories as traditional ice charts. The machine‐learning forecasts were compared to persistence, which is the assumption that the sea ice does not change over the forecasting period. The machine‐learning forecasts were found to improve upon persistence in periods of substantial change. In addition, compared to the dynamical model, the k‐nearest neighbor algorithm was found to improve upon the 7‐day forecast during a period of small sea ice variations. The fully convolutional network provided similar quality as the dynamical forecast. The study shows that there is a potential for sea ice predictions using machine‐learning methods. |
format |
Article in Journal/Newspaper |
author |
Fritzner, Sindre Markus Graversen, Rune Christensen, Kai Håkon |
spellingShingle |
Fritzner, Sindre Markus Graversen, Rune Christensen, Kai Håkon Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application |
author_facet |
Fritzner, Sindre Markus Graversen, Rune Christensen, Kai Håkon |
author_sort |
Fritzner, Sindre Markus |
title |
Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application |
title_short |
Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application |
title_full |
Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application |
title_fullStr |
Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application |
title_full_unstemmed |
Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application |
title_sort |
assessment of high‐resolution dynamical and machine learning models for prediction of sea ice concentration in a regional application |
publishDate |
2020 |
url |
http://hdl.handle.net/10852/80863 http://urn.nb.no/URN:NBN:no-83947 https://doi.org/10.1029/2020JC016277 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
2169-9275 |
op_relation |
http://urn.nb.no/URN:NBN:no-83947 Fritzner, Sindre Markus Graversen, Rune Christensen, Kai Håkon . Assessment of High‐Resolution Dynamical and Machine Learning Models for Prediction of Sea Ice Concentration in a Regional Application. Journal of Geophysical Research (JGR): Oceans. 2020, 125(11), 1-23 http://hdl.handle.net/10852/80863 1843517 info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal of Geophysical Research (JGR): Oceans&rft.volume=125&rft.spage=1&rft.date=2020 Journal of Geophysical Research (JGR): Oceans 125 11 1 23 https://doi.org/10.1029/2020JC016277 URN:NBN:no-83947 Fulltext https://www.duo.uio.no/bitstream/handle/10852/80863/2/2020JC016277.pdf |
op_doi |
https://doi.org/10.1029/2020JC016277 |
container_title |
Journal of Geophysical Research: Oceans |
container_volume |
125 |
container_issue |
11 |
_version_ |
1766189748616953856 |