Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System
To understand the Arctic environment, which is closely related to sea ice and to reduce potential risks, reliable sea ice forecasts are indispensable. A practical, lightweight yet effective assimilation scheme of sea ice concentration based on Optimal Interpolation is designed and adopted in an oper...
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ftmdpi:oai:mdpi.com:/2072-4292/15/5/1274/ 2023-08-20T04:04:04+02:00 Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System Qiuli Shao Qi Shu Bin Xiao Lujun Zhang Xunqiang Yin Fangli Qiao agris 2023-02-25 application/pdf https://doi.org/10.3390/rs15051274 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs15051274 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 15; Issue 5; Pages: 1274 sea ice concentration data assimilation global ocean forecasting system Text 2023 ftmdpi https://doi.org/10.3390/rs15051274 2023-08-01T08:59:38Z To understand the Arctic environment, which is closely related to sea ice and to reduce potential risks, reliable sea ice forecasts are indispensable. A practical, lightweight yet effective assimilation scheme of sea ice concentration based on Optimal Interpolation is designed and adopted in an operational global 1/10° surface wave-tide-circulation coupled ocean model (FIO-COM10) forecasting system to improve Arctic sea ice forecasting. Twin numerical experiments with and without data assimilation are designed for the simulation of the year 2019, and 5-day real-time forecasts for 2021 are implemented to study the sea ice forecast ability. The results show that the large biases in the simulation and forecast of sea ice concentration are remarkably reduced due to satellite observation uncertainty levels by data assimilation, indicating the high efficiency of the data assimilation scheme. The most significant improvement occurs in the marginal ice zones. The sea surface temperature bias averaged over the marginal ice zones is also reduced by 0.9 °C. Sea ice concentration assimilation has a profound effect on improving forecasting ability. The Root Mean Square Error and Integrated Ice-Edge Error are reduced to the level of the independent satellite observation at least for 24-h forecast, and sea ice forecast by FIO-COM10 has better performance than the persistence forecasts in summer and autumn. Text Arctic Sea ice MDPI Open Access Publishing Arctic Remote Sensing 15 5 1274 |
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language |
English |
topic |
sea ice concentration data assimilation global ocean forecasting system |
spellingShingle |
sea ice concentration data assimilation global ocean forecasting system Qiuli Shao Qi Shu Bin Xiao Lujun Zhang Xunqiang Yin Fangli Qiao Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System |
topic_facet |
sea ice concentration data assimilation global ocean forecasting system |
description |
To understand the Arctic environment, which is closely related to sea ice and to reduce potential risks, reliable sea ice forecasts are indispensable. A practical, lightweight yet effective assimilation scheme of sea ice concentration based on Optimal Interpolation is designed and adopted in an operational global 1/10° surface wave-tide-circulation coupled ocean model (FIO-COM10) forecasting system to improve Arctic sea ice forecasting. Twin numerical experiments with and without data assimilation are designed for the simulation of the year 2019, and 5-day real-time forecasts for 2021 are implemented to study the sea ice forecast ability. The results show that the large biases in the simulation and forecast of sea ice concentration are remarkably reduced due to satellite observation uncertainty levels by data assimilation, indicating the high efficiency of the data assimilation scheme. The most significant improvement occurs in the marginal ice zones. The sea surface temperature bias averaged over the marginal ice zones is also reduced by 0.9 °C. Sea ice concentration assimilation has a profound effect on improving forecasting ability. The Root Mean Square Error and Integrated Ice-Edge Error are reduced to the level of the independent satellite observation at least for 24-h forecast, and sea ice forecast by FIO-COM10 has better performance than the persistence forecasts in summer and autumn. |
format |
Text |
author |
Qiuli Shao Qi Shu Bin Xiao Lujun Zhang Xunqiang Yin Fangli Qiao |
author_facet |
Qiuli Shao Qi Shu Bin Xiao Lujun Zhang Xunqiang Yin Fangli Qiao |
author_sort |
Qiuli Shao |
title |
Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System |
title_short |
Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System |
title_full |
Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System |
title_fullStr |
Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System |
title_full_unstemmed |
Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast System |
title_sort |
arctic sea ice concentration assimilation in an operational global 1/10° ocean forecast system |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15051274 |
op_coverage |
agris |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Remote Sensing; Volume 15; Issue 5; Pages: 1274 |
op_relation |
https://dx.doi.org/10.3390/rs15051274 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs15051274 |
container_title |
Remote Sensing |
container_volume |
15 |
container_issue |
5 |
container_start_page |
1274 |
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1774714495581552640 |