SAR-Based Sea Ice Drift and Lagrangian Tracking for Evaluating Sea Ice Drift Forecast Models
We use sea ice drift information obtained from Synthetic Aperture Radar (SAR) observations to assess the usability of sea ice drift forecast models for multi-day sea ice analysis. This work demonstrates the capabilities of a vector approach on a small case study of twenty analysed SAR-scene pairs in...
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ftdlr:oai:elib.dlr.de:194039 2024-05-19T07:37:55+00:00 SAR-Based Sea Ice Drift and Lagrangian Tracking for Evaluating Sea Ice Drift Forecast Models Bathmann, Martin Frost, Anja Wiehle, Stefan Spreen, Gunnar 2023-06-05 https://elib.dlr.de/194039/ https://www.igsoc.org/wp-content/uploads/2023/06/procabstracts_80.html#A4125 unknown Bathmann, Martin und Frost, Anja und Wiehle, Stefan und Spreen, Gunnar (2023) SAR-Based Sea Ice Drift and Lagrangian Tracking for Evaluating Sea Ice Drift Forecast Models. International Symposium on Sea Ice 2023, 2023-06-04 - 2023-06-09, Bremerhaven, Germany. SAR-Signalverarbeitung Konferenzbeitrag NonPeerReviewed 2023 ftdlr 2024-04-25T01:05:25Z We use sea ice drift information obtained from Synthetic Aperture Radar (SAR) observations to assess the usability of sea ice drift forecast models for multi-day sea ice analysis. This work demonstrates the capabilities of a vector approach on a small case study of twenty analysed SAR-scene pairs in winter 2022/2023 in the Baffin Bay with 12.5km and 0.5km grid spacing. Finding optimal shipping routes through sea ice becomes increasingly important for navigation in polar regions. Sea ice drift forecasts such as TOPAZ4 and neXtSIM provide a prediction of the sea ice situation several days (up to 10) into the future. We apply an approach that combines methods from the interdisciplinary field of environmental physics, geoinformation technology and remote sensing. Sea ice drift vector fields are obtained from successive Sentinel-1 image pairs using the phase correlation technique applied in a hierarchical resolution pyramid. The derived drift is compared to historical multi-day sea ice drift forecast of TOPAZ4 and neXtSIM, provided in the EU CMEMS (Copernicus Marine Environment Monitoring Service) Artic analysis and forecast products PHYS_002_001_a and PHY_ICE_002_011. The forecast model trajectories and SAR-based measurements are both calculated starting from a regular grid. This allows working either in a raster or a vector model. We use the vector model for data processing to benefit from high flexibility and floating-point number coordinate resolution. An object-oriented-programming (OOP) approach, a topology, hashing and spatial indexing yield a computational performance that can compete with raster analysis. Forecast model trajectories are derived with Lagrangian Tracking. The deformation parameters divergence, vorticity and shear are calculated by applying Sobel kernels. With this developed workflow, sea ice motion and deformation predicted by TOPAZ4 and neXtSIM can be evaluated by SAR-based drift measurements. The present work is realized in the context of the FAST-CAST 2 project, funded under grant 19F2191A ... Conference Object Baffin Bay Baffin Bay Baffin Sea ice German Aerospace Center: elib - DLR electronic library |
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German Aerospace Center: elib - DLR electronic library |
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topic |
SAR-Signalverarbeitung |
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SAR-Signalverarbeitung Bathmann, Martin Frost, Anja Wiehle, Stefan Spreen, Gunnar SAR-Based Sea Ice Drift and Lagrangian Tracking for Evaluating Sea Ice Drift Forecast Models |
topic_facet |
SAR-Signalverarbeitung |
description |
We use sea ice drift information obtained from Synthetic Aperture Radar (SAR) observations to assess the usability of sea ice drift forecast models for multi-day sea ice analysis. This work demonstrates the capabilities of a vector approach on a small case study of twenty analysed SAR-scene pairs in winter 2022/2023 in the Baffin Bay with 12.5km and 0.5km grid spacing. Finding optimal shipping routes through sea ice becomes increasingly important for navigation in polar regions. Sea ice drift forecasts such as TOPAZ4 and neXtSIM provide a prediction of the sea ice situation several days (up to 10) into the future. We apply an approach that combines methods from the interdisciplinary field of environmental physics, geoinformation technology and remote sensing. Sea ice drift vector fields are obtained from successive Sentinel-1 image pairs using the phase correlation technique applied in a hierarchical resolution pyramid. The derived drift is compared to historical multi-day sea ice drift forecast of TOPAZ4 and neXtSIM, provided in the EU CMEMS (Copernicus Marine Environment Monitoring Service) Artic analysis and forecast products PHYS_002_001_a and PHY_ICE_002_011. The forecast model trajectories and SAR-based measurements are both calculated starting from a regular grid. This allows working either in a raster or a vector model. We use the vector model for data processing to benefit from high flexibility and floating-point number coordinate resolution. An object-oriented-programming (OOP) approach, a topology, hashing and spatial indexing yield a computational performance that can compete with raster analysis. Forecast model trajectories are derived with Lagrangian Tracking. The deformation parameters divergence, vorticity and shear are calculated by applying Sobel kernels. With this developed workflow, sea ice motion and deformation predicted by TOPAZ4 and neXtSIM can be evaluated by SAR-based drift measurements. The present work is realized in the context of the FAST-CAST 2 project, funded under grant 19F2191A ... |
format |
Conference Object |
author |
Bathmann, Martin Frost, Anja Wiehle, Stefan Spreen, Gunnar |
author_facet |
Bathmann, Martin Frost, Anja Wiehle, Stefan Spreen, Gunnar |
author_sort |
Bathmann, Martin |
title |
SAR-Based Sea Ice Drift and Lagrangian Tracking for Evaluating Sea Ice Drift Forecast Models |
title_short |
SAR-Based Sea Ice Drift and Lagrangian Tracking for Evaluating Sea Ice Drift Forecast Models |
title_full |
SAR-Based Sea Ice Drift and Lagrangian Tracking for Evaluating Sea Ice Drift Forecast Models |
title_fullStr |
SAR-Based Sea Ice Drift and Lagrangian Tracking for Evaluating Sea Ice Drift Forecast Models |
title_full_unstemmed |
SAR-Based Sea Ice Drift and Lagrangian Tracking for Evaluating Sea Ice Drift Forecast Models |
title_sort |
sar-based sea ice drift and lagrangian tracking for evaluating sea ice drift forecast models |
publishDate |
2023 |
url |
https://elib.dlr.de/194039/ https://www.igsoc.org/wp-content/uploads/2023/06/procabstracts_80.html#A4125 |
genre |
Baffin Bay Baffin Bay Baffin Sea ice |
genre_facet |
Baffin Bay Baffin Bay Baffin Sea ice |
op_relation |
Bathmann, Martin und Frost, Anja und Wiehle, Stefan und Spreen, Gunnar (2023) SAR-Based Sea Ice Drift and Lagrangian Tracking for Evaluating Sea Ice Drift Forecast Models. International Symposium on Sea Ice 2023, 2023-06-04 - 2023-06-09, Bremerhaven, Germany. |
_version_ |
1799477317473402880 |