Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation
Source at https://doi.org/10.1017/jog.2018.33 . 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 t...
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Online Access: | https://hdl.handle.net/10037/13969 https://doi.org/10.1017/jog.2018.33 |
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ftunivtroemsoe:oai:munin.uit.no:10037/13969 2023-05-15T14:27:59+02:00 Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation Fritzner, Sindre Markus Graversen, Rune Wang, Keguang Christensen, Kai Håkon 2018-04-25 https://hdl.handle.net/10037/13969 https://doi.org/10.1017/jog.2018.33 eng eng Cambridge University Press (CUP) Fritzner, S.M. (2020). On sea-ice forecasting. (Doctoral thesis). https://hdl.handle.net/10037/18141 . Journal of Glaciology info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ https://www.cambridge.org/core/journals/journal-of-glaciology/article/comparison-between-a-multivariate-nudging-method-and-the-ensemble-kalman-filter-for-seaice-data-assimilation/6B5BAE0A22A5828F22402 Fritzner, S.M., Graversen, R.G., Wang, K. & Christensen, K.H. (2018). Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation. Journal of Glaciology, 64(245), 387-396. https://doi.org/ 10.1017/jog.2018.33 FRIDAID 1599288 doi:10.1017/jog.2018.33 0022-1430 1727-5652 https://hdl.handle.net/10037/13969 openAccess VDP::Mathematics and natural science: 400::Geosciences: 450::Quaternary geology glaciology: 465 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Kvartærgeologi glasiologi: 465 Arctic glaciology sea ice sea-ice modelling Journal article Tidsskriftartikkel Peer reviewed 2018 ftunivtroemsoe https://doi.org/10.1017/jog.2018.33 2021-06-25T17:56:09Z Source at https://doi.org/10.1017/jog.2018.33 . 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 Arctic Journal of Glaciology Sea ice University of Tromsø: Munin Open Research Archive Arctic Journal of Glaciology 64 245 387 396 |
institution |
Open Polar |
collection |
University of Tromsø: Munin Open Research Archive |
op_collection_id |
ftunivtroemsoe |
language |
English |
topic |
VDP::Mathematics and natural science: 400::Geosciences: 450::Quaternary geology glaciology: 465 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Kvartærgeologi glasiologi: 465 Arctic glaciology sea ice sea-ice modelling |
spellingShingle |
VDP::Mathematics and natural science: 400::Geosciences: 450::Quaternary geology glaciology: 465 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Kvartærgeologi glasiologi: 465 Arctic glaciology sea ice sea-ice modelling Fritzner, Sindre Markus Graversen, Rune Wang, Keguang Christensen, Kai Håkon Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation |
topic_facet |
VDP::Mathematics and natural science: 400::Geosciences: 450::Quaternary geology glaciology: 465 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Kvartærgeologi glasiologi: 465 Arctic glaciology sea ice sea-ice modelling |
description |
Source at https://doi.org/10.1017/jog.2018.33 . 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 Markus Graversen, Rune Wang, Keguang Christensen, Kai Håkon |
author_facet |
Fritzner, Sindre Markus Graversen, Rune Wang, Keguang Christensen, Kai Håkon |
author_sort |
Fritzner, Sindre Markus |
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 |
https://hdl.handle.net/10037/13969 https://doi.org/10.1017/jog.2018.33 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Arctic Journal of Glaciology Sea ice |
genre_facet |
Arctic Arctic Journal of Glaciology Sea ice |
op_relation |
Fritzner, S.M. (2020). On sea-ice forecasting. (Doctoral thesis). https://hdl.handle.net/10037/18141 . Journal of Glaciology info:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/ https://www.cambridge.org/core/journals/journal-of-glaciology/article/comparison-between-a-multivariate-nudging-method-and-the-ensemble-kalman-filter-for-seaice-data-assimilation/6B5BAE0A22A5828F22402 Fritzner, S.M., Graversen, R.G., Wang, K. & Christensen, K.H. (2018). Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation. Journal of Glaciology, 64(245), 387-396. https://doi.org/ 10.1017/jog.2018.33 FRIDAID 1599288 doi:10.1017/jog.2018.33 0022-1430 1727-5652 https://hdl.handle.net/10037/13969 |
op_rights |
openAccess |
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 |
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1766302091274354688 |