An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities
An algorithmic approach, based on satellite-derived sea-surface (“skin”) salinities (SSS), is proposed to correct for errors in SSS retrievals and convert these skin salinities into comparable in-situ (“bulk”) salinities for the top-5 m of the subpolar and Arctic Oceans. In preparation for routine a...
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ftmdpi:oai:mdpi.com:/2072-4292/14/6/1418/ 2023-08-20T04:03:55+02:00 An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities David Trossman Eric Bayler 2022-03-15 application/pdf https://doi.org/10.3390/rs14061418 EN eng Multidisciplinary Digital Publishing Institute AI Remote Sensing https://dx.doi.org/10.3390/rs14061418 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 6; Pages: 1418 salinity SMAP skin-effect bias air-sea Arctic ocean machine-learning Text 2022 ftmdpi https://doi.org/10.3390/rs14061418 2023-08-01T04:27:49Z An algorithmic approach, based on satellite-derived sea-surface (“skin”) salinities (SSS), is proposed to correct for errors in SSS retrievals and convert these skin salinities into comparable in-situ (“bulk”) salinities for the top-5 m of the subpolar and Arctic Oceans. In preparation for routine assimilation into operational ocean forecast models, Soil Moisture Active Passive (SMAP) satellite Level-2 SSS observations are transformed using Argo float data from the top-5 m of the ocean to address the mismatch between the skin depth of satellite L-band SSS measurements (∼1 cm) and the thickness of top model layers (typically at least 1 m). Separate from the challenge of Argo float availability in most of the subpolar and Arctic Oceans, satellite-derived SSS products for these regions currently are not suitable for assimilation for a myriad of other reasons, including erroneous ancillary air-sea forcing/flux products. In the subpolar and Arctic Oceans, the root-mean-square error (RMSE) between the SMAP SSS product and several in-situ salinity observational data sets for the top-5 m is greater than 1.5 pss (Practical Salinity Scale), which can be larger than their temporal variability. Thus, we train a machine-learning algorithm (called a Generalized Additive Model) on in-situ salinities from the top-5 m and an independent air-sea forcing/flux product to convert the SMAP SSS into bulk-salinities, correct biases, and quantify their standard errors. The RMSE between these corrected bulk-salinities and in-situ measurements is less than 1 pss in open ocean regions. Barring persistently problematic data near coasts and ice-pack edges, the corrected bulk-salinity data are in better agreement with in-situ data than their SMAP SSS equivalent. Text Arctic Arctic Ocean ice pack MDPI Open Access Publishing Arctic Arctic Ocean Remote Sensing 14 6 1418 |
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Open Polar |
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MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
salinity SMAP skin-effect bias air-sea Arctic ocean machine-learning |
spellingShingle |
salinity SMAP skin-effect bias air-sea Arctic ocean machine-learning David Trossman Eric Bayler An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities |
topic_facet |
salinity SMAP skin-effect bias air-sea Arctic ocean machine-learning |
description |
An algorithmic approach, based on satellite-derived sea-surface (“skin”) salinities (SSS), is proposed to correct for errors in SSS retrievals and convert these skin salinities into comparable in-situ (“bulk”) salinities for the top-5 m of the subpolar and Arctic Oceans. In preparation for routine assimilation into operational ocean forecast models, Soil Moisture Active Passive (SMAP) satellite Level-2 SSS observations are transformed using Argo float data from the top-5 m of the ocean to address the mismatch between the skin depth of satellite L-band SSS measurements (∼1 cm) and the thickness of top model layers (typically at least 1 m). Separate from the challenge of Argo float availability in most of the subpolar and Arctic Oceans, satellite-derived SSS products for these regions currently are not suitable for assimilation for a myriad of other reasons, including erroneous ancillary air-sea forcing/flux products. In the subpolar and Arctic Oceans, the root-mean-square error (RMSE) between the SMAP SSS product and several in-situ salinity observational data sets for the top-5 m is greater than 1.5 pss (Practical Salinity Scale), which can be larger than their temporal variability. Thus, we train a machine-learning algorithm (called a Generalized Additive Model) on in-situ salinities from the top-5 m and an independent air-sea forcing/flux product to convert the SMAP SSS into bulk-salinities, correct biases, and quantify their standard errors. The RMSE between these corrected bulk-salinities and in-situ measurements is less than 1 pss in open ocean regions. Barring persistently problematic data near coasts and ice-pack edges, the corrected bulk-salinity data are in better agreement with in-situ data than their SMAP SSS equivalent. |
format |
Text |
author |
David Trossman Eric Bayler |
author_facet |
David Trossman Eric Bayler |
author_sort |
David Trossman |
title |
An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities |
title_short |
An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities |
title_full |
An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities |
title_fullStr |
An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities |
title_full_unstemmed |
An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities |
title_sort |
algorithm to bias-correct and transform arctic smap-derived skin salinities into bulk surface salinities |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14061418 |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
Arctic Arctic Ocean ice pack |
genre_facet |
Arctic Arctic Ocean ice pack |
op_source |
Remote Sensing; Volume 14; Issue 6; Pages: 1418 |
op_relation |
AI Remote Sensing https://dx.doi.org/10.3390/rs14061418 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs14061418 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
6 |
container_start_page |
1418 |
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1774714343689027584 |