Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation
Increasing ship traffic and human activity in the Arctic has led to a growing demand for accurate Arctic weather forecast. High-quality forecasts obtained by models are dependent on accurate initial states achieved by assimilation of observations. In this study, a multi-variate nudging (MVN) method...
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ftdoajarticles:oai:doaj.org/article:d5228c2900cb4263869eb3429ed0ac48 2023-05-15T14:53:43+02:00 Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation SINDRE M. FRITZNER RUNE G. GRAVERSEN KEGUANG WANG KAI H. CHRISTENSEN 2018-06-01T00:00:00Z https://doi.org/10.1017/jog.2018.33 https://doaj.org/article/d5228c2900cb4263869eb3429ed0ac48 EN eng Cambridge University Press https://www.cambridge.org/core/product/identifier/S0022143018000333/type/journal_article https://doaj.org/toc/0022-1430 https://doaj.org/toc/1727-5652 doi:10.1017/jog.2018.33 0022-1430 1727-5652 https://doaj.org/article/d5228c2900cb4263869eb3429ed0ac48 Journal of Glaciology, Vol 64, Pp 387-396 (2018) Arctic glaciology sea ice sea-ice modelling Environmental sciences GE1-350 Meteorology. Climatology QC851-999 article 2018 ftdoajarticles https://doi.org/10.1017/jog.2018.33 2023-03-12T01:30:59Z Increasing ship traffic and human activity in the Arctic has led to a growing demand for accurate Arctic weather forecast. High-quality forecasts obtained by models are dependent on accurate initial states achieved by assimilation of observations. In this study, a multi-variate nudging (MVN) method for assimilation of sea-ice variables is introduced. The MVN assimilation method includes procedures for multivariate update of sea-ice volume and concentration, and for extrapolation of observational information spatially. The MVN assimilation scheme is compared with the Ensemble Kalman Filter (EnKF) using the Los Alamos Sea Ice Model. Two multi-variate experiments are conducted: in the first experiment, sea-ice thickness from the European Space Agency's Soil Moisture and Ocean Salinity mission is assimilated, and in the second experiment, sea-ice concentration from the ocean and Sea Ice Satellite Application Facility is assimilated. The multivariate effects are cross-validated by comparing the model with non-assimilated observations. It is found that the simple and computationally cheap MVN method shows comparable skills to the more complicated and expensive EnKF method for multivariate update. In addition, we show that when few observations are available, the MVN method is a significant model improvement compared to the version based on one-dimensional sea-ice concentration assimilation. Article in Journal/Newspaper Arctic Journal of Glaciology Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Journal of Glaciology 64 245 387 396 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Arctic glaciology sea ice sea-ice modelling Environmental sciences GE1-350 Meteorology. Climatology QC851-999 |
spellingShingle |
Arctic glaciology sea ice sea-ice modelling Environmental sciences GE1-350 Meteorology. Climatology QC851-999 SINDRE M. FRITZNER RUNE G. GRAVERSEN KEGUANG WANG KAI H. CHRISTENSEN Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation |
topic_facet |
Arctic glaciology sea ice sea-ice modelling Environmental sciences GE1-350 Meteorology. Climatology QC851-999 |
description |
Increasing ship traffic and human activity in the Arctic has led to a growing demand for accurate Arctic weather forecast. High-quality forecasts obtained by models are dependent on accurate initial states achieved by assimilation of observations. In this study, a multi-variate nudging (MVN) method for assimilation of sea-ice variables is introduced. The MVN assimilation method includes procedures for multivariate update of sea-ice volume and concentration, and for extrapolation of observational information spatially. The MVN assimilation scheme is compared with the Ensemble Kalman Filter (EnKF) using the Los Alamos Sea Ice Model. Two multi-variate experiments are conducted: in the first experiment, sea-ice thickness from the European Space Agency's Soil Moisture and Ocean Salinity mission is assimilated, and in the second experiment, sea-ice concentration from the ocean and Sea Ice Satellite Application Facility is assimilated. The multivariate effects are cross-validated by comparing the model with non-assimilated observations. It is found that the simple and computationally cheap MVN method shows comparable skills to the more complicated and expensive EnKF method for multivariate update. In addition, we show that when few observations are available, the MVN method is a significant model improvement compared to the version based on one-dimensional sea-ice concentration assimilation. |
format |
Article in Journal/Newspaper |
author |
SINDRE M. FRITZNER RUNE G. GRAVERSEN KEGUANG WANG KAI H. CHRISTENSEN |
author_facet |
SINDRE M. FRITZNER RUNE G. GRAVERSEN KEGUANG WANG KAI H. CHRISTENSEN |
author_sort |
SINDRE M. FRITZNER |
title |
Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation |
title_short |
Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation |
title_full |
Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation |
title_fullStr |
Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation |
title_full_unstemmed |
Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation |
title_sort |
comparison between a multi-variate nudging method and the ensemble kalman filter for sea-ice data assimilation |
publisher |
Cambridge University Press |
publishDate |
2018 |
url |
https://doi.org/10.1017/jog.2018.33 https://doaj.org/article/d5228c2900cb4263869eb3429ed0ac48 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Journal of Glaciology Sea ice |
genre_facet |
Arctic Journal of Glaciology Sea ice |
op_source |
Journal of Glaciology, Vol 64, Pp 387-396 (2018) |
op_relation |
https://www.cambridge.org/core/product/identifier/S0022143018000333/type/journal_article https://doaj.org/toc/0022-1430 https://doaj.org/toc/1727-5652 doi:10.1017/jog.2018.33 0022-1430 1727-5652 https://doaj.org/article/d5228c2900cb4263869eb3429ed0ac48 |
op_doi |
https://doi.org/10.1017/jog.2018.33 |
container_title |
Journal of Glaciology |
container_volume |
64 |
container_issue |
245 |
container_start_page |
387 |
op_container_end_page |
396 |
_version_ |
1766325301714878464 |