Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration
In this paper, the assimilation of binary observations calculated from synthetic aperture radar (SAR) images of sea ice is investigated. Ice and water observations are obtained from a set of SAR images by thresholding ice and water probabilities calculated using a supervised maximum likelihood estim...
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ftdoajarticles:oai:doaj.org/article:1da3780bdd7b4d228423b73191cc0993 2023-05-15T18:17:19+02:00 Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration K. Andrea Scott Zahra Ashouri Mark Buehner Lynn Pogson Tom Carrieres 2015-09-01T00:00:00Z https://doi.org/10.3402/tellusa.v67.27218 https://doaj.org/article/1da3780bdd7b4d228423b73191cc0993 EN eng Stockholm University Press http://www.tellusa.net/index.php/tellusa/article/view/27218/pdf_55 https://doaj.org/toc/1600-0870 1600-0870 doi:10.3402/tellusa.v67.27218 https://doaj.org/article/1da3780bdd7b4d228423b73191cc0993 Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 67, Iss 0, Pp 1-17 (2015) sea ice data assimilation synthetic aperture radar binary Bayesian probability forward model Oceanography GC1-1581 Meteorology. Climatology QC851-999 article 2015 ftdoajarticles https://doi.org/10.3402/tellusa.v67.27218 2022-12-30T22:22:37Z In this paper, the assimilation of binary observations calculated from synthetic aperture radar (SAR) images of sea ice is investigated. Ice and water observations are obtained from a set of SAR images by thresholding ice and water probabilities calculated using a supervised maximum likelihood estimator (MLE). These ice and water observations are then assimilated in combination with ice concentration from passive microwave imagery for the purpose of estimating sea ice concentration. Due to the fact that the observations are binary, consisting of zeros and ones, while the state vector is a continuous variable (ice concentration), the forward model used to map the state vector to the observation space requires special consideration. Both linear and non-linear forward models were investigated. In both cases, the assimilation of SAR data was able to produce ice concentration analyses in closer agreement with image analysis charts than when assimilating passive microwave data only. When both passive microwave and SAR data are assimilated, the bias between the ice concentration analyses and the ice concentration from ice charts is 19.78%, as compared to 26.72% when only passive microwave data are assimilated. The method presented here for the assimilation of SAR data could be applied to other binary observations, such as ice/water information from visual/infrared sensors. Article in Journal/Newspaper Sea ice Directory of Open Access Journals: DOAJ Articles Tellus A: Dynamic Meteorology and Oceanography 67 1 27218 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
sea ice data assimilation synthetic aperture radar binary Bayesian probability forward model Oceanography GC1-1581 Meteorology. Climatology QC851-999 |
spellingShingle |
sea ice data assimilation synthetic aperture radar binary Bayesian probability forward model Oceanography GC1-1581 Meteorology. Climatology QC851-999 K. Andrea Scott Zahra Ashouri Mark Buehner Lynn Pogson Tom Carrieres Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration |
topic_facet |
sea ice data assimilation synthetic aperture radar binary Bayesian probability forward model Oceanography GC1-1581 Meteorology. Climatology QC851-999 |
description |
In this paper, the assimilation of binary observations calculated from synthetic aperture radar (SAR) images of sea ice is investigated. Ice and water observations are obtained from a set of SAR images by thresholding ice and water probabilities calculated using a supervised maximum likelihood estimator (MLE). These ice and water observations are then assimilated in combination with ice concentration from passive microwave imagery for the purpose of estimating sea ice concentration. Due to the fact that the observations are binary, consisting of zeros and ones, while the state vector is a continuous variable (ice concentration), the forward model used to map the state vector to the observation space requires special consideration. Both linear and non-linear forward models were investigated. In both cases, the assimilation of SAR data was able to produce ice concentration analyses in closer agreement with image analysis charts than when assimilating passive microwave data only. When both passive microwave and SAR data are assimilated, the bias between the ice concentration analyses and the ice concentration from ice charts is 19.78%, as compared to 26.72% when only passive microwave data are assimilated. The method presented here for the assimilation of SAR data could be applied to other binary observations, such as ice/water information from visual/infrared sensors. |
format |
Article in Journal/Newspaper |
author |
K. Andrea Scott Zahra Ashouri Mark Buehner Lynn Pogson Tom Carrieres |
author_facet |
K. Andrea Scott Zahra Ashouri Mark Buehner Lynn Pogson Tom Carrieres |
author_sort |
K. Andrea Scott |
title |
Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration |
title_short |
Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration |
title_full |
Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration |
title_fullStr |
Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration |
title_full_unstemmed |
Assimilation of ice and water observations from SAR imagery to improve estimates of sea ice concentration |
title_sort |
assimilation of ice and water observations from sar imagery to improve estimates of sea ice concentration |
publisher |
Stockholm University Press |
publishDate |
2015 |
url |
https://doi.org/10.3402/tellusa.v67.27218 https://doaj.org/article/1da3780bdd7b4d228423b73191cc0993 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
Tellus: Series A, Dynamic Meteorology and Oceanography, Vol 67, Iss 0, Pp 1-17 (2015) |
op_relation |
http://www.tellusa.net/index.php/tellusa/article/view/27218/pdf_55 https://doaj.org/toc/1600-0870 1600-0870 doi:10.3402/tellusa.v67.27218 https://doaj.org/article/1da3780bdd7b4d228423b73191cc0993 |
op_doi |
https://doi.org/10.3402/tellusa.v67.27218 |
container_title |
Tellus A: Dynamic Meteorology and Oceanography |
container_volume |
67 |
container_issue |
1 |
container_start_page |
27218 |
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1766191461870600192 |