Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation
ABSTRACT 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...
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Cambridge University Press (CUP)
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crcambridgeupr:10.1017/jog.2018.33 2024-06-23T07:50:05+00:00 Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation FRITZNER, SINDRE M. GRAVERSEN, RUNE G. WANG, KEGUANG CHRISTENSEN, KAI H. 2018 http://dx.doi.org/10.1017/jog.2018.33 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143018000333 en eng Cambridge University Press (CUP) http://creativecommons.org/licenses/by/4.0/ Journal of Glaciology volume 64, issue 245, page 387-396 ISSN 0022-1430 1727-5652 journal-article 2018 crcambridgeupr https://doi.org/10.1017/jog.2018.33 2024-06-05T04:03:27Z ABSTRACT 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 Cambridge University Press Arctic Journal of Glaciology 64 245 387 396 |
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Open Polar |
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Cambridge University Press |
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crcambridgeupr |
language |
English |
description |
ABSTRACT 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 |
FRITZNER, SINDRE M. GRAVERSEN, RUNE G. WANG, KEGUANG CHRISTENSEN, KAI H. |
spellingShingle |
FRITZNER, SINDRE M. GRAVERSEN, RUNE G. WANG, KEGUANG CHRISTENSEN, KAI H. Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation |
author_facet |
FRITZNER, SINDRE M. GRAVERSEN, RUNE G. WANG, KEGUANG CHRISTENSEN, KAI H. |
author_sort |
FRITZNER, SINDRE M. |
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 (CUP) |
publishDate |
2018 |
url |
http://dx.doi.org/10.1017/jog.2018.33 https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0022143018000333 |
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 volume 64, issue 245, page 387-396 ISSN 0022-1430 1727-5652 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
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_ |
1802640855548297216 |