An Adaptive Approach to Derive Sea Ice Draft from Upward-Looking Acoustic Doppler Current Profilers (ADCPs), Validated by Upward-Looking Sonar (ULS) Data

Moored upward-looking Acoustic Doppler Current Profilers (ADCPs) can be used to observe sea ice draft. While previous studies relied on the availability of auxiliary pressure sensors to measure the instrument depth of the ADCP, we present an adaptive approach that infers instrument depth from ADCP b...

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Published in:Remote Sensing
Main Authors: Hans Jakob Belter, Thomas Krumpen, Markus A. Janout, Ed Ross, Christian Haas
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2021
Subjects:
Online Access:https://doi.org/10.3390/rs13214335
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spelling ftmdpi:oai:mdpi.com:/2072-4292/13/21/4335/ 2023-08-20T04:07:51+02:00 An Adaptive Approach to Derive Sea Ice Draft from Upward-Looking Acoustic Doppler Current Profilers (ADCPs), Validated by Upward-Looking Sonar (ULS) Data Hans Jakob Belter Thomas Krumpen Markus A. Janout Ed Ross Christian Haas 2021-10-28 application/pdf https://doi.org/10.3390/rs13214335 EN eng Multidisciplinary Digital Publishing Institute Ocean Remote Sensing https://dx.doi.org/10.3390/rs13214335 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 21; Pages: 4335 sea ice thickness sonar Laptev Sea Text 2021 ftmdpi https://doi.org/10.3390/rs13214335 2023-08-01T03:05:40Z Moored upward-looking Acoustic Doppler Current Profilers (ADCPs) can be used to observe sea ice draft. While previous studies relied on the availability of auxiliary pressure sensors to measure the instrument depth of the ADCP, we present an adaptive approach that infers instrument depth from ADCP bottom track (BT) mode measurements of error velocity and range. The ADCP-derived ice draft time series are validated with data from adjacent Upward-Looking Sonar (ULS) moorings. We demonstrate that this method can be used to obtain daily mean sea ice draft time series that, on average, are within 20% of ULS-derived draft time series. ULS and ADCP ice draft time series were observed by four moorings in the Laptev Sea and show correlations between 0.7 and 0.9. This new approach is not a substitute for high-frequency, high-precision ULS measurements of ice draft but it provides a low-cost opportunity to derive daily mean ice draft time series accessing existing ADCP data that have not been not used for that purpose to date. This method has the potential to close data gaps and extend existing ice draft time series in all ice-covered regions and supports the validation of sea ice thickness products from satellite missions such as CryoSat-2, SMOS or ENVISAT. Text laptev Laptev Sea Sea ice MDPI Open Access Publishing Laptev Sea Remote Sensing 13 21 4335
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic sea ice thickness
sonar
Laptev Sea
spellingShingle sea ice thickness
sonar
Laptev Sea
Hans Jakob Belter
Thomas Krumpen
Markus A. Janout
Ed Ross
Christian Haas
An Adaptive Approach to Derive Sea Ice Draft from Upward-Looking Acoustic Doppler Current Profilers (ADCPs), Validated by Upward-Looking Sonar (ULS) Data
topic_facet sea ice thickness
sonar
Laptev Sea
description Moored upward-looking Acoustic Doppler Current Profilers (ADCPs) can be used to observe sea ice draft. While previous studies relied on the availability of auxiliary pressure sensors to measure the instrument depth of the ADCP, we present an adaptive approach that infers instrument depth from ADCP bottom track (BT) mode measurements of error velocity and range. The ADCP-derived ice draft time series are validated with data from adjacent Upward-Looking Sonar (ULS) moorings. We demonstrate that this method can be used to obtain daily mean sea ice draft time series that, on average, are within 20% of ULS-derived draft time series. ULS and ADCP ice draft time series were observed by four moorings in the Laptev Sea and show correlations between 0.7 and 0.9. This new approach is not a substitute for high-frequency, high-precision ULS measurements of ice draft but it provides a low-cost opportunity to derive daily mean ice draft time series accessing existing ADCP data that have not been not used for that purpose to date. This method has the potential to close data gaps and extend existing ice draft time series in all ice-covered regions and supports the validation of sea ice thickness products from satellite missions such as CryoSat-2, SMOS or ENVISAT.
format Text
author Hans Jakob Belter
Thomas Krumpen
Markus A. Janout
Ed Ross
Christian Haas
author_facet Hans Jakob Belter
Thomas Krumpen
Markus A. Janout
Ed Ross
Christian Haas
author_sort Hans Jakob Belter
title An Adaptive Approach to Derive Sea Ice Draft from Upward-Looking Acoustic Doppler Current Profilers (ADCPs), Validated by Upward-Looking Sonar (ULS) Data
title_short An Adaptive Approach to Derive Sea Ice Draft from Upward-Looking Acoustic Doppler Current Profilers (ADCPs), Validated by Upward-Looking Sonar (ULS) Data
title_full An Adaptive Approach to Derive Sea Ice Draft from Upward-Looking Acoustic Doppler Current Profilers (ADCPs), Validated by Upward-Looking Sonar (ULS) Data
title_fullStr An Adaptive Approach to Derive Sea Ice Draft from Upward-Looking Acoustic Doppler Current Profilers (ADCPs), Validated by Upward-Looking Sonar (ULS) Data
title_full_unstemmed An Adaptive Approach to Derive Sea Ice Draft from Upward-Looking Acoustic Doppler Current Profilers (ADCPs), Validated by Upward-Looking Sonar (ULS) Data
title_sort adaptive approach to derive sea ice draft from upward-looking acoustic doppler current profilers (adcps), validated by upward-looking sonar (uls) data
publisher Multidisciplinary Digital Publishing Institute
publishDate 2021
url https://doi.org/10.3390/rs13214335
geographic Laptev Sea
geographic_facet Laptev Sea
genre laptev
Laptev Sea
Sea ice
genre_facet laptev
Laptev Sea
Sea ice
op_source Remote Sensing; Volume 13; Issue 21; Pages: 4335
op_relation Ocean Remote Sensing
https://dx.doi.org/10.3390/rs13214335
op_rights https://creativecommons.org/licenses/by/4.0/
op_doi https://doi.org/10.3390/rs13214335
container_title Remote Sensing
container_volume 13
container_issue 21
container_start_page 4335
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