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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Online Access: | https://doi.org/10.3390/rs14225837 |
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ftmdpi:oai:mdpi.com:/2072-4292/14/22/5837/ 2023-08-20T04:04:02+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 agris 2022-11-17 application/pdf https://doi.org/10.3390/rs14225837 EN eng Multidisciplinary Digital Publishing Institute AI Remote Sensing https://dx.doi.org/10.3390/rs14225837 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 22; Pages: 5837 data-driven models short-term sea ice forecasting deep learning computer vision U-Net remote sensing satellite imagery analysis Arctic sea ice Text 2022 ftmdpi https://doi.org/10.3390/rs14225837 2023-08-01T07:23:48Z 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. Text Arctic Barents Sea Global warming Labrador Sea laptev Laptev Sea Sea ice MDPI Open Access Publishing Arctic Barents Sea Laptev Sea Remote Sensing 14 22 5837 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
data-driven models short-term sea ice forecasting deep learning computer vision U-Net remote sensing satellite imagery analysis Arctic sea ice |
spellingShingle |
data-driven models short-term sea ice forecasting deep learning computer vision U-Net remote sensing satellite imagery analysis Arctic sea ice 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 satellite imagery analysis Arctic sea ice |
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 |
Text |
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 |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14225837 |
op_coverage |
agris |
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; Volume 14; Issue 22; Pages: 5837 |
op_relation |
AI Remote Sensing https://dx.doi.org/10.3390/rs14225837 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs14225837 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
22 |
container_start_page |
5837 |
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1774714460244541440 |