Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting
Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient f...
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2022
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Online Access: | https://doi.org/10.3390/rs14225837 https://doaj.org/article/ff8d86b5fefd489d978a618f0e75a73b |
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ftdoajarticles:oai:doaj.org/article:ff8d86b5fefd489d978a618f0e75a73b 2023-05-15T14:57:11+02:00 Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting Timofey Grigoryev Polina Verezemskaya Mikhail Krinitskiy Nikita Anikin Alexander Gavrikov Ilya Trofimov Nikita Balabin Aleksei Shpilman Andrei Eremchenko Sergey Gulev Evgeny Burnaev Vladimir Vanovskiy 2022-11-01T00:00:00Z https://doi.org/10.3390/rs14225837 https://doaj.org/article/ff8d86b5fefd489d978a618f0e75a73b EN eng MDPI AG https://www.mdpi.com/2072-4292/14/22/5837 https://doaj.org/toc/2072-4292 doi:10.3390/rs14225837 2072-4292 https://doaj.org/article/ff8d86b5fefd489d978a618f0e75a73b Remote Sensing, Vol 14, Iss 5837, p 5837 (2022) data-driven models short-term sea ice forecasting deep learning computer vision U-Net remote sensing Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14225837 2022-12-30T20:13:19Z Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies have exploited different deep learning models alongside classical approaches for predicting sea ice concentration in the Arctic. However, only a few focus on daily operational forecasts and consider the real-time availability of data needed for marine operations. In this article, we aim to close this gap and investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin, and we can improve the model’s quality by using additional weather data and training on multiple regions to ensure its generalization abilities. As a practical outcome, we build a fast and flexible tool that produces operational sea ice forecasts in the Barents Sea, the Labrador Sea, and the Laptev Sea regions. Article in Journal/Newspaper Arctic Barents Sea Global warming Labrador Sea laptev Laptev Sea Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Barents Sea Laptev Sea Remote Sensing 14 22 5837 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
data-driven models short-term sea ice forecasting deep learning computer vision U-Net remote sensing Science Q |
spellingShingle |
data-driven models short-term sea ice forecasting deep learning computer vision U-Net remote sensing Science Q Timofey Grigoryev Polina Verezemskaya Mikhail Krinitskiy Nikita Anikin Alexander Gavrikov Ilya Trofimov Nikita Balabin Aleksei Shpilman Andrei Eremchenko Sergey Gulev Evgeny Burnaev Vladimir Vanovskiy Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting |
topic_facet |
data-driven models short-term sea ice forecasting deep learning computer vision U-Net remote sensing Science Q |
description |
Global warming has made the Arctic increasingly available for marine operations and created a demand for reliable operational sea ice forecasts to increase safety. Because ocean-ice numerical models are highly computationally intensive, relatively lightweight ML-based methods may be more efficient for sea ice forecasting. Many studies have exploited different deep learning models alongside classical approaches for predicting sea ice concentration in the Arctic. However, only a few focus on daily operational forecasts and consider the real-time availability of data needed for marine operations. In this article, we aim to close this gap and investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin, and we can improve the model’s quality by using additional weather data and training on multiple regions to ensure its generalization abilities. As a practical outcome, we build a fast and flexible tool that produces operational sea ice forecasts in the Barents Sea, the Labrador Sea, and the Laptev Sea regions. |
format |
Article in Journal/Newspaper |
author |
Timofey Grigoryev Polina Verezemskaya Mikhail Krinitskiy Nikita Anikin Alexander Gavrikov Ilya Trofimov Nikita Balabin Aleksei Shpilman Andrei Eremchenko Sergey Gulev Evgeny Burnaev Vladimir Vanovskiy |
author_facet |
Timofey Grigoryev Polina Verezemskaya Mikhail Krinitskiy Nikita Anikin Alexander Gavrikov Ilya Trofimov Nikita Balabin Aleksei Shpilman Andrei Eremchenko Sergey Gulev Evgeny Burnaev Vladimir Vanovskiy |
author_sort |
Timofey Grigoryev |
title |
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting |
title_short |
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting |
title_full |
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting |
title_fullStr |
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting |
title_full_unstemmed |
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting |
title_sort |
data-driven short-term daily operational sea ice regional forecasting |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14225837 https://doaj.org/article/ff8d86b5fefd489d978a618f0e75a73b |
geographic |
Arctic Barents Sea Laptev Sea |
geographic_facet |
Arctic Barents Sea Laptev Sea |
genre |
Arctic Barents Sea Global warming Labrador Sea laptev Laptev Sea Sea ice |
genre_facet |
Arctic Barents Sea Global warming Labrador Sea laptev Laptev Sea Sea ice |
op_source |
Remote Sensing, Vol 14, Iss 5837, p 5837 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/22/5837 https://doaj.org/toc/2072-4292 doi:10.3390/rs14225837 2072-4292 https://doaj.org/article/ff8d86b5fefd489d978a618f0e75a73b |
op_doi |
https://doi.org/10.3390/rs14225837 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
22 |
container_start_page |
5837 |
_version_ |
1766329267878100992 |