Impact of surface conditions on thin sea ice concentration estimate from passive microwave observations

Ice concentration retrieved from spaceborne passive microwave observations is a prime input to operational sea ice monitoring programs, numerical weather prediction and global climate models. However, it is usually underestimated by existing algorithms due to surface conditions, especially in case o...

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Published in:Remote Sensing of Environment
Main Authors: Shokr, M., Kaleschke, L.
Format: Article in Journal/Newspaper
Language:English
Published: 2012
Subjects:
Online Access:http://hdl.handle.net/11858/00-001M-0000-0019-92FA-F
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spelling ftpubman:oai:pure.mpg.de:item_2030722 2024-09-15T18:35:06+00:00 Impact of surface conditions on thin sea ice concentration estimate from passive microwave observations Shokr, M. Kaleschke, L. 2012-06 http://hdl.handle.net/11858/00-001M-0000-0019-92FA-F eng eng info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2012.01.005 http://hdl.handle.net/11858/00-001M-0000-0019-92FA-F REMOTE SENSING OF ENVIRONMENT info:eu-repo/semantics/article 2012 ftpubman https://doi.org/10.1016/j.rse.2012.01.005 2024-07-31T09:31:28Z Ice concentration retrieved from spaceborne passive microwave observations is a prime input to operational sea ice monitoring programs, numerical weather prediction and global climate models. However, it is usually underestimated by existing algorithms due to surface conditions, especially in case of young ice types. Evaluation of those algorithms identifies errors in concentration estimates but does not necessarily link them to the adverse surface conditions. The present study is an attempt to establish those links for young ice (<25 cm) thick It uses measurements of microwave emission from artificially grown sea ice in an outdoor tank and calculates ice concentration using five established algorithms: NT, Bootstrap (BSA), NT2, ASI and ECICE. Since the actual concentration is known (100%), then any deviation from this value is considered an error and can be linked to the observed surface conditions, which are usually caused by weather events. Those conditions were acquired on hourly or daily basis. Results identify key conditions that lead to underestimation of ice concentration. They include surface refreezing, slush, snow settling following fresh snowfall, and falling precipitation in different forms. The study shows also that NT and NT2 are most affected by surface processes while BSA performs better. ASI is much less affected because it uses the high frequency channel (e.g. SSM/I 85 GHz), which is sensitive only to processes within the top snow layer. ECICE, with its probabilistic and ensemble approach shows also good results under most surface conditions. Dry or wet snow does not lead to significant difference in ice concentration estimate. The study also aims at validation of ECICE. (c) 2012 Elsevier Inc. All rights reserved. Article in Journal/Newspaper Sea ice Max Planck Society: MPG.PuRe Remote Sensing of Environment 121 36 50
institution Open Polar
collection Max Planck Society: MPG.PuRe
op_collection_id ftpubman
language English
description Ice concentration retrieved from spaceborne passive microwave observations is a prime input to operational sea ice monitoring programs, numerical weather prediction and global climate models. However, it is usually underestimated by existing algorithms due to surface conditions, especially in case of young ice types. Evaluation of those algorithms identifies errors in concentration estimates but does not necessarily link them to the adverse surface conditions. The present study is an attempt to establish those links for young ice (<25 cm) thick It uses measurements of microwave emission from artificially grown sea ice in an outdoor tank and calculates ice concentration using five established algorithms: NT, Bootstrap (BSA), NT2, ASI and ECICE. Since the actual concentration is known (100%), then any deviation from this value is considered an error and can be linked to the observed surface conditions, which are usually caused by weather events. Those conditions were acquired on hourly or daily basis. Results identify key conditions that lead to underestimation of ice concentration. They include surface refreezing, slush, snow settling following fresh snowfall, and falling precipitation in different forms. The study shows also that NT and NT2 are most affected by surface processes while BSA performs better. ASI is much less affected because it uses the high frequency channel (e.g. SSM/I 85 GHz), which is sensitive only to processes within the top snow layer. ECICE, with its probabilistic and ensemble approach shows also good results under most surface conditions. Dry or wet snow does not lead to significant difference in ice concentration estimate. The study also aims at validation of ECICE. (c) 2012 Elsevier Inc. All rights reserved.
format Article in Journal/Newspaper
author Shokr, M.
Kaleschke, L.
spellingShingle Shokr, M.
Kaleschke, L.
Impact of surface conditions on thin sea ice concentration estimate from passive microwave observations
author_facet Shokr, M.
Kaleschke, L.
author_sort Shokr, M.
title Impact of surface conditions on thin sea ice concentration estimate from passive microwave observations
title_short Impact of surface conditions on thin sea ice concentration estimate from passive microwave observations
title_full Impact of surface conditions on thin sea ice concentration estimate from passive microwave observations
title_fullStr Impact of surface conditions on thin sea ice concentration estimate from passive microwave observations
title_full_unstemmed Impact of surface conditions on thin sea ice concentration estimate from passive microwave observations
title_sort impact of surface conditions on thin sea ice concentration estimate from passive microwave observations
publishDate 2012
url http://hdl.handle.net/11858/00-001M-0000-0019-92FA-F
genre Sea ice
genre_facet Sea ice
op_source REMOTE SENSING OF ENVIRONMENT
op_relation info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2012.01.005
http://hdl.handle.net/11858/00-001M-0000-0019-92FA-F
op_doi https://doi.org/10.1016/j.rse.2012.01.005
container_title Remote Sensing of Environment
container_volume 121
container_start_page 36
op_container_end_page 50
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