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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/5/1274 |
| _version_ | 1797614416022208512 |
|---|---|
| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T07:12:07Z |
| format | Article |
| id | doaj.art-0aed6332bf8a46328eec9f9fe26bb6df |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T07:12:07Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0aed6332bf8a46328eec9f9fe26bb6df2023-11-17T08:30:49ZengMDPI AGRemote Sensing2072-42922023-02-01155127410.3390/rs15051274Arctic Sea Ice Concentration Assimilation in an Operational Global 1/10° Ocean Forecast SystemQiuli Shao0Qi Shu1Bin Xiao2Lujun Zhang3Xunqiang Yin4Fangli Qiao5Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Qingdao 266061, ChinaFirst Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, ChinaFirst Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, ChinaSchool of Atmospheric Sciences, Nanjing University, Nanjing 210093, ChinaFirst Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, ChinaFirst Institute of Oceanography, and Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, ChinaTo 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.https://www.mdpi.com/2072-4292/15/5/1274sea ice concentrationdata assimilationglobal ocean forecasting system |
| spellingShingle | 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 Remote Sensing sea ice concentration data assimilation global ocean forecasting system |
| title | 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_short | 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 |
| topic | sea ice concentration data assimilation global ocean forecasting system |
| url | https://www.mdpi.com/2072-4292/15/5/1274 |
| work_keys_str_mv | AT qiulishao arcticseaiceconcentrationassimilationinanoperationalglobal110oceanforecastsystem AT qishu arcticseaiceconcentrationassimilationinanoperationalglobal110oceanforecastsystem AT binxiao arcticseaiceconcentrationassimilationinanoperationalglobal110oceanforecastsystem AT lujunzhang arcticseaiceconcentrationassimilationinanoperationalglobal110oceanforecastsystem AT xunqiangyin arcticseaiceconcentrationassimilationinanoperationalglobal110oceanforecastsystem AT fangliqiao arcticseaiceconcentrationassimilationinanoperationalglobal110oceanforecastsystem |