Mitigation of model bias influences on wave data assimilation with multiple assimilation systems using WaveWatch III v5.16 and SWAN v41.20
<p>High-quality wave prediction with a numerical wave model is of societal value. To initialize the wave model, wave data assimilation (WDA) is necessary to combine the model and observations. Due to imperfect numerical schemes and approximated physical processes, a wave model is always biased...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-03-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://www.geosci-model-dev.net/13/1035/2020/gmd-13-1035-2020.pdf |
| _version_ | 1818035636224917504 |
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| author | J. Li J. Li S. Zhang S. Zhang S. Zhang S. Zhang |
| author_facet | J. Li J. Li S. Zhang S. Zhang S. Zhang S. Zhang |
| author_sort | J. Li |
| collection | DOAJ |
| description | <p>High-quality wave prediction with a numerical wave model is of societal value. To initialize the wave model, wave data assimilation (WDA) is
necessary to combine the model and observations. Due to imperfect numerical schemes and approximated physical processes, a wave model is always
biased in relation to the real world. In this study, two assimilation systems are first developed using two nearly independent wave models; then,
“perfect” and “biased” assimilation frameworks based on the two assimilation systems are designed to reveal the uncertainties of WDA. A series
of biased assimilation experiments is conducted to systematically examine the adverse impact of model bias on WDA. A statistical approach based
on the results from multiple assimilation systems is explored to carry out bias correction, by which the final wave analysis is significantly
improved with the merits of individual assimilation systems. The framework with multiple assimilation systems provides an effective platform to
improve wave analyses and predictions and help identify model deficits, thereby improving the model.</p> |
| first_indexed | 2024-12-10T06:58:12Z |
| format | Article |
| id | doaj.art-e3bc6759221e4a91b6ff7db85b9f9a9b |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-12-10T06:58:12Z |
| publishDate | 2020-03-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-e3bc6759221e4a91b6ff7db85b9f9a9b2022-12-22T01:58:23ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032020-03-01131035105410.5194/gmd-13-1035-2020Mitigation of model bias influences on wave data assimilation with multiple assimilation systems using WaveWatch III v5.16 and SWAN v41.20J. Li0J. Li1S. Zhang2S. Zhang3S. Zhang4S. Zhang5Key Laboratory of Physical Oceanography, Ministry of Education, Institute for Advanced Ocean Study, Frontiers Science Center for Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China, Qingdao, 266100, ChinaCollege of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, ChinaKey Laboratory of Physical Oceanography, Ministry of Education, Institute for Advanced Ocean Study, Frontiers Science Center for Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China, Qingdao, 266100, ChinaCollege of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, ChinaPilot National Laboratory for Marine Science and Technology (QNLM), Qingdao, 266100, ChinaInternational Laboratory for High-Resolution Earth System Model and Prediction (iHESP), Qingdao, 266100, China<p>High-quality wave prediction with a numerical wave model is of societal value. To initialize the wave model, wave data assimilation (WDA) is necessary to combine the model and observations. Due to imperfect numerical schemes and approximated physical processes, a wave model is always biased in relation to the real world. In this study, two assimilation systems are first developed using two nearly independent wave models; then, “perfect” and “biased” assimilation frameworks based on the two assimilation systems are designed to reveal the uncertainties of WDA. A series of biased assimilation experiments is conducted to systematically examine the adverse impact of model bias on WDA. A statistical approach based on the results from multiple assimilation systems is explored to carry out bias correction, by which the final wave analysis is significantly improved with the merits of individual assimilation systems. The framework with multiple assimilation systems provides an effective platform to improve wave analyses and predictions and help identify model deficits, thereby improving the model.</p>https://www.geosci-model-dev.net/13/1035/2020/gmd-13-1035-2020.pdf |
| spellingShingle | J. Li J. Li S. Zhang S. Zhang S. Zhang S. Zhang Mitigation of model bias influences on wave data assimilation with multiple assimilation systems using WaveWatch III v5.16 and SWAN v41.20 Geoscientific Model Development |
| title | Mitigation of model bias influences on wave data assimilation with multiple assimilation systems using WaveWatch III v5.16 and SWAN v41.20 |
| title_full | Mitigation of model bias influences on wave data assimilation with multiple assimilation systems using WaveWatch III v5.16 and SWAN v41.20 |
| title_fullStr | Mitigation of model bias influences on wave data assimilation with multiple assimilation systems using WaveWatch III v5.16 and SWAN v41.20 |
| title_full_unstemmed | Mitigation of model bias influences on wave data assimilation with multiple assimilation systems using WaveWatch III v5.16 and SWAN v41.20 |
| title_short | Mitigation of model bias influences on wave data assimilation with multiple assimilation systems using WaveWatch III v5.16 and SWAN v41.20 |
| title_sort | mitigation of model bias influences on wave data assimilation with multiple assimilation systems using wavewatch iii v5 16 and swan v41 20 |
| url | https://www.geosci-model-dev.net/13/1035/2020/gmd-13-1035-2020.pdf |
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