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...
Published in: | Remote Sensing |
---|---|
Main Authors: | , , , |
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 |
---|---|
record_format |
openpolar |
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 |
_version_ |
1810294284251824128 |