Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model
Local analytical optimal nudging (LAON) is introduced and thoroughly evaluated for assimilating the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration (SIC) in the Norwegian High-resolution pan-Arctic ocean and sea ice Prediction System (NorHAPS). NorHAPS is a developing high-res...
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Copernicus Publications
2023
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Online Access: | https://doi.org/10.5194/tc-17-4487-2023 https://doaj.org/article/c467475131584a37a41866e0ba3e44e9 |
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ftdoajarticles:oai:doaj.org/article:c467475131584a37a41866e0ba3e44e9 2023-11-12T04:12:26+01:00 Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model K. Wang A. Ali C. Wang 2023-10-01T00:00:00Z https://doi.org/10.5194/tc-17-4487-2023 https://doaj.org/article/c467475131584a37a41866e0ba3e44e9 EN eng Copernicus Publications https://tc.copernicus.org/articles/17/4487/2023/tc-17-4487-2023.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-17-4487-2023 1994-0416 1994-0424 https://doaj.org/article/c467475131584a37a41866e0ba3e44e9 The Cryosphere, Vol 17, Pp 4487-4510 (2023) Environmental sciences GE1-350 Geology QE1-996.5 article 2023 ftdoajarticles https://doi.org/10.5194/tc-17-4487-2023 2023-10-29T00:38:27Z Local analytical optimal nudging (LAON) is introduced and thoroughly evaluated for assimilating the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration (SIC) in the Norwegian High-resolution pan-Arctic ocean and sea ice Prediction System (NorHAPS). NorHAPS is a developing high-resolution (3–5 km) pan-Arctic coupled ocean and sea ice modeling and prediction system based on the HYbrid Coordinate Ocean Model (HYCOM version 2.2.98) and the Los Alamos multi-category sea ice model (CICE version 5.1.2), with the LAON for data assimilation. In this study, our focus is on the LAON assimilation of AMSR2 SIC, which is designed to update the model SIC in every time step such that the analysis will eventually reach the optimal estimate. The SIC innovation (observation minus model) is designed to be proportionally distributed to the multiple sea ice categories. A hindcast experiment is performed with and without the LAON assimilation for the period 1 January 2021 to 30 April 2022, in which the extra computational cost for the LAON assimilation is about 5 % of the free run without assimilation. The results show that the LAON assimilation greatly improves the simulated sea ice concentration, extent, area, thickness, and volume, as well as the sea surface temperature (SST). It also produces significantly more accurate sea ice edge and marginal zone (MIZ) than the observed AMSR2 SIC that is assimilated when evaluated against the Norwegian Ice Service (NIS) ice chart. The results are also compared with the Copernicus Marine Environment Monitoring Service (CMEMS) operational SIC analyses from NEMO, TOPAZ4, and neXtSIM, which use ensemble Kalman filters and direct insertion for data assimilation. It is shown that the LAON assimilation produces significantly lower integrated ice edge error (IIEE) and integrated MIZ error (IME) than the CMEMS SIC analyses when evaluated against the NIS ice chart. LAON also produces a continuous and smooth evolution of sub-daily SIC, which avoids abrupt jumps often seen in other ... Article in Journal/Newspaper Arctic Arctic Ocean Sea ice The Cryosphere Directory of Open Access Journals: DOAJ Articles The Cryosphere 17 10 4487 4510 |
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Directory of Open Access Journals: DOAJ Articles |
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language |
English |
topic |
Environmental sciences GE1-350 Geology QE1-996.5 |
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Environmental sciences GE1-350 Geology QE1-996.5 K. Wang A. Ali C. Wang Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model |
topic_facet |
Environmental sciences GE1-350 Geology QE1-996.5 |
description |
Local analytical optimal nudging (LAON) is introduced and thoroughly evaluated for assimilating the Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice concentration (SIC) in the Norwegian High-resolution pan-Arctic ocean and sea ice Prediction System (NorHAPS). NorHAPS is a developing high-resolution (3–5 km) pan-Arctic coupled ocean and sea ice modeling and prediction system based on the HYbrid Coordinate Ocean Model (HYCOM version 2.2.98) and the Los Alamos multi-category sea ice model (CICE version 5.1.2), with the LAON for data assimilation. In this study, our focus is on the LAON assimilation of AMSR2 SIC, which is designed to update the model SIC in every time step such that the analysis will eventually reach the optimal estimate. The SIC innovation (observation minus model) is designed to be proportionally distributed to the multiple sea ice categories. A hindcast experiment is performed with and without the LAON assimilation for the period 1 January 2021 to 30 April 2022, in which the extra computational cost for the LAON assimilation is about 5 % of the free run without assimilation. The results show that the LAON assimilation greatly improves the simulated sea ice concentration, extent, area, thickness, and volume, as well as the sea surface temperature (SST). It also produces significantly more accurate sea ice edge and marginal zone (MIZ) than the observed AMSR2 SIC that is assimilated when evaluated against the Norwegian Ice Service (NIS) ice chart. The results are also compared with the Copernicus Marine Environment Monitoring Service (CMEMS) operational SIC analyses from NEMO, TOPAZ4, and neXtSIM, which use ensemble Kalman filters and direct insertion for data assimilation. It is shown that the LAON assimilation produces significantly lower integrated ice edge error (IIEE) and integrated MIZ error (IME) than the CMEMS SIC analyses when evaluated against the NIS ice chart. LAON also produces a continuous and smooth evolution of sub-daily SIC, which avoids abrupt jumps often seen in other ... |
format |
Article in Journal/Newspaper |
author |
K. Wang A. Ali C. Wang |
author_facet |
K. Wang A. Ali C. Wang |
author_sort |
K. Wang |
title |
Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model |
title_short |
Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model |
title_full |
Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model |
title_fullStr |
Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model |
title_full_unstemmed |
Local analytical optimal nudging for assimilating AMSR2 sea ice concentration in a high-resolution pan-Arctic coupled ocean (HYCOM 2.2.98) and sea ice (CICE 5.1.2) model |
title_sort |
local analytical optimal nudging for assimilating amsr2 sea ice concentration in a high-resolution pan-arctic coupled ocean (hycom 2.2.98) and sea ice (cice 5.1.2) model |
publisher |
Copernicus Publications |
publishDate |
2023 |
url |
https://doi.org/10.5194/tc-17-4487-2023 https://doaj.org/article/c467475131584a37a41866e0ba3e44e9 |
genre |
Arctic Arctic Ocean Sea ice The Cryosphere |
genre_facet |
Arctic Arctic Ocean Sea ice The Cryosphere |
op_source |
The Cryosphere, Vol 17, Pp 4487-4510 (2023) |
op_relation |
https://tc.copernicus.org/articles/17/4487/2023/tc-17-4487-2023.pdf https://doaj.org/toc/1994-0416 https://doaj.org/toc/1994-0424 doi:10.5194/tc-17-4487-2023 1994-0416 1994-0424 https://doaj.org/article/c467475131584a37a41866e0ba3e44e9 |
op_doi |
https://doi.org/10.5194/tc-17-4487-2023 |
container_title |
The Cryosphere |
container_volume |
17 |
container_issue |
10 |
container_start_page |
4487 |
op_container_end_page |
4510 |
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1782330976009977856 |