Multi-sensor data merging of sea ice concentration and thickness
With the rapid change in the Arctic sea ice, a large number of sea ice observations have been collected in recent years, and it is expected that an even larger number of such observations will emerge in the coming years. To make the best use of these observations, in this paper we develop a multi-se...
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ftarcticportal:oai:generic.eprints.org:2709 2023-12-10T09:38:59+01:00 Multi-sensor data merging of sea ice concentration and thickness Wang, Keguang Lavergne, Thomas Dinessen, Frode 2020-03 application/pdf http://library.arcticportal.org/2709/ http://library.arcticportal.org/2709/1/A2001001.pdf en eng Polar Research Institute of China - PRIC http://library.arcticportal.org/2709/1/A2001001.pdf Wang, Keguang and Lavergne, Thomas and Dinessen, Frode (2020) Multi-sensor data merging of sea ice concentration and thickness. Advances in Polar Science, 31 (1). pp. 1-13. Cryosphere Oceans Article PeerReviewed 2020 ftarcticportal 2023-11-15T23:54:41Z With the rapid change in the Arctic sea ice, a large number of sea ice observations have been collected in recent years, and it is expected that an even larger number of such observations will emerge in the coming years. To make the best use of these observations, in this paper we develop a multi-sensor optimal data merging (MODM) method to merge any number of different sea ice observations. Since such merged data are independent on model forecast, they are valid for model initialization and model validation. Based on the maximum likelihood estimation theory, we prove that any model assimilated with the merged data is equivalent to assimilating the original multi-sensor data. This greatly facilitates sea ice data assimilation, particularly for operational forecast with limited computational resources. We apply the MODM method to merge sea ice concentration (SIC) and sea ice thickness (SIT), respectively, in the Arctic. For SIC merging, the Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) data are merged together with the Norwegian Ice Service ice chart. This substantially reduces the uncertainties at the ice edge and in the coastal areas. For SIT merging, the daily Soil Moisture and Ocean Salinity (SMOS) data is merged with the weekly-mean merged CryoSat-2 and SMOS (CS2SMOS) data. This generates a new daily CS2SMOS SIT data with better spatial coverage for the whole Arctic. Article in Journal/Newspaper Advances in Polar Science Arctic Polar Science Polar Science Sea ice Arctic Portal Library Arctic |
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Open Polar |
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Arctic Portal Library |
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ftarcticportal |
language |
English |
topic |
Cryosphere Oceans |
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Cryosphere Oceans Wang, Keguang Lavergne, Thomas Dinessen, Frode Multi-sensor data merging of sea ice concentration and thickness |
topic_facet |
Cryosphere Oceans |
description |
With the rapid change in the Arctic sea ice, a large number of sea ice observations have been collected in recent years, and it is expected that an even larger number of such observations will emerge in the coming years. To make the best use of these observations, in this paper we develop a multi-sensor optimal data merging (MODM) method to merge any number of different sea ice observations. Since such merged data are independent on model forecast, they are valid for model initialization and model validation. Based on the maximum likelihood estimation theory, we prove that any model assimilated with the merged data is equivalent to assimilating the original multi-sensor data. This greatly facilitates sea ice data assimilation, particularly for operational forecast with limited computational resources. We apply the MODM method to merge sea ice concentration (SIC) and sea ice thickness (SIT), respectively, in the Arctic. For SIC merging, the Special Sensor Microwave Imager/Sounder (SSMIS) and Advanced Microwave Scanning Radiometer 2 (AMSR2) data are merged together with the Norwegian Ice Service ice chart. This substantially reduces the uncertainties at the ice edge and in the coastal areas. For SIT merging, the daily Soil Moisture and Ocean Salinity (SMOS) data is merged with the weekly-mean merged CryoSat-2 and SMOS (CS2SMOS) data. This generates a new daily CS2SMOS SIT data with better spatial coverage for the whole Arctic. |
format |
Article in Journal/Newspaper |
author |
Wang, Keguang Lavergne, Thomas Dinessen, Frode |
author_facet |
Wang, Keguang Lavergne, Thomas Dinessen, Frode |
author_sort |
Wang, Keguang |
title |
Multi-sensor data merging of sea ice concentration and thickness |
title_short |
Multi-sensor data merging of sea ice concentration and thickness |
title_full |
Multi-sensor data merging of sea ice concentration and thickness |
title_fullStr |
Multi-sensor data merging of sea ice concentration and thickness |
title_full_unstemmed |
Multi-sensor data merging of sea ice concentration and thickness |
title_sort |
multi-sensor data merging of sea ice concentration and thickness |
publisher |
Polar Research Institute of China - PRIC |
publishDate |
2020 |
url |
http://library.arcticportal.org/2709/ http://library.arcticportal.org/2709/1/A2001001.pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Advances in Polar Science Arctic Polar Science Polar Science Sea ice |
genre_facet |
Advances in Polar Science Arctic Polar Science Polar Science Sea ice |
op_relation |
http://library.arcticportal.org/2709/1/A2001001.pdf Wang, Keguang and Lavergne, Thomas and Dinessen, Frode (2020) Multi-sensor data merging of sea ice concentration and thickness. Advances in Polar Science, 31 (1). pp. 1-13. |
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