A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS

<jats:p>Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MO...

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Published in:Remote Sensing
Main Authors: Preußer, Andreas, Heinemann, Günther, Schefczyk, Lukas, Willmes, Sascha
Format: Article in Journal/Newspaper
Language:unknown
Published: MDPI AG 2022
Subjects:
Online Access:https://epic.awi.de/id/eprint/57471/
https://epic.awi.de/id/eprint/57471/1/Preusseretal2022_remotesensing-14-02036.pdf
https://hdl.handle.net/10013/epic.7a721655-c864-4529-b1c0-bbdaaa4abc76
id ftawi:oai:epic.awi.de:57471
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spelling ftawi:oai:epic.awi.de:57471 2024-09-15T17:52:13+00:00 A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS Preußer, Andreas Heinemann, Günther Schefczyk, Lukas Willmes, Sascha 2022-04-23 application/pdf https://epic.awi.de/id/eprint/57471/ https://epic.awi.de/id/eprint/57471/1/Preusseretal2022_remotesensing-14-02036.pdf https://hdl.handle.net/10013/epic.7a721655-c864-4529-b1c0-bbdaaa4abc76 unknown MDPI AG https://epic.awi.de/id/eprint/57471/1/Preusseretal2022_remotesensing-14-02036.pdf Preußer, A. orcid:0000-0003-0134-6890 , Heinemann, G. , Schefczyk, L. and Willmes, S. (2022) A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS , Remote Sensing, 14 (9), p. 2036 . doi:10.3390/rs14092036 <https://doi.org/10.3390/rs14092036> , hdl:10013/epic.7a721655-c864-4529-b1c0-bbdaaa4abc76 EPIC3Remote Sensing, MDPI AG, 14(9), pp. 2036-2036, ISSN: 2072-4292 Article NonPeerReviewed 2022 ftawi https://doi.org/10.3390/rs14092036 2024-06-24T04:30:12Z <jats:p>Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.</jats:p> Article in Journal/Newspaper Arctic Archipelago Arctic Ocean Canadian Arctic Archipelago laptev Laptev Sea Sea ice Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) Remote Sensing 14 9 2036
institution Open Polar
collection Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center)
op_collection_id ftawi
language unknown
description <jats:p>Knowledge of the wintertime sea-ice production in Arctic polynyas is an important requirement for estimations of the dense water formation, which drives vertical mixing in the upper ocean. Satellite-based techniques incorporating relatively high resolution thermal-infrared data from MODIS in combination with atmospheric reanalysis data have proven to be a strong tool to monitor large and regularly forming polynyas and to resolve narrow thin-ice areas (i.e., leads) along the shelf-breaks and across the entire Arctic Ocean. However, the selection of the atmospheric data sets has a large influence on derived polynya characteristics due to their impact on the calculation of the heat loss to the atmosphere, which is determined by the local thin-ice thickness. In order to overcome this methodical ambiguity, we present a MODIS-assisted temperature adjustment (MATA) algorithm that yields corrections of the 2 m air temperature and hence decreases differences between the atmospheric input data sets. The adjustment algorithm is based on atmospheric model simulations. We focus on the Laptev Sea region for detailed case studies on the developed algorithm and present time series of polynya characteristics in the winter season 2019/2020. It shows that the application of the empirically derived correction decreases the difference between different utilized atmospheric products significantly from 49% to 23%. Additional filter strategies are applied that aim at increasing the capability to include leads in the quasi-daily and persistence-filtered thin-ice thickness composites. More generally, the winter of 2019/2020 features high polynya activity in the eastern Arctic and less activity in the Canadian Arctic Archipelago, presumably as a result of the particularly strong polar vortex in early 2020.</jats:p>
format Article in Journal/Newspaper
author Preußer, Andreas
Heinemann, Günther
Schefczyk, Lukas
Willmes, Sascha
spellingShingle Preußer, Andreas
Heinemann, Günther
Schefczyk, Lukas
Willmes, Sascha
A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS
author_facet Preußer, Andreas
Heinemann, Günther
Schefczyk, Lukas
Willmes, Sascha
author_sort Preußer, Andreas
title A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS
title_short A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS
title_full A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS
title_fullStr A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS
title_full_unstemmed A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS
title_sort model-based temperature adjustment scheme for wintertime sea-ice production retrievals from modis
publisher MDPI AG
publishDate 2022
url https://epic.awi.de/id/eprint/57471/
https://epic.awi.de/id/eprint/57471/1/Preusseretal2022_remotesensing-14-02036.pdf
https://hdl.handle.net/10013/epic.7a721655-c864-4529-b1c0-bbdaaa4abc76
genre Arctic Archipelago
Arctic Ocean
Canadian Arctic Archipelago
laptev
Laptev Sea
Sea ice
genre_facet Arctic Archipelago
Arctic Ocean
Canadian Arctic Archipelago
laptev
Laptev Sea
Sea ice
op_source EPIC3Remote Sensing, MDPI AG, 14(9), pp. 2036-2036, ISSN: 2072-4292
op_relation https://epic.awi.de/id/eprint/57471/1/Preusseretal2022_remotesensing-14-02036.pdf
Preußer, A. orcid:0000-0003-0134-6890 , Heinemann, G. , Schefczyk, L. and Willmes, S. (2022) A Model-Based Temperature Adjustment Scheme for Wintertime Sea-Ice Production Retrievals from MODIS , Remote Sensing, 14 (9), p. 2036 . doi:10.3390/rs14092036 <https://doi.org/10.3390/rs14092036> , hdl:10013/epic.7a721655-c864-4529-b1c0-bbdaaa4abc76
op_doi https://doi.org/10.3390/rs14092036
container_title Remote Sensing
container_volume 14
container_issue 9
container_start_page 2036
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