Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models
The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehi...
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ftmdpi:oai:mdpi.com:/2072-4292/13/5/831/ 2023-08-20T04:03:59+02:00 Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models Jorge Vazquez-Cuervo Chelle Gentemann Wenqing Tang Dustin Carroll Hong Zhang Dimitris Menemenlis Jose Gomez-Valdes Marouan Bouali Michael Steele agris 2021-02-24 application/pdf https://doi.org/10.3390/rs13050831 EN eng Multidisciplinary Digital Publishing Institute Ocean Remote Sensing https://dx.doi.org/10.3390/rs13050831 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 13; Issue 5; Pages: 831 sea surface salinity validation coastal Text 2021 ftmdpi https://doi.org/10.3390/rs13050831 2023-08-01T01:08:13Z The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate products that use data from the Soil Moisture Active Passive (SMAP) satellite. To examine possible issues in resolving mesoscale-to-submesoscale variability, comparisons were also made with two versions of the Estimating the Circulation and Climate of the Ocean (ECCO) model (Carroll, D; Menmenlis, D; Zhang, H.). The results indicate that the three SMAP products resolve the runoff signal associated with the Yukon River, with high correlation between SMAP products and Saildrone SSS. Spectral slopes, overall, replicate the −2.0 slopes associated with mesoscale-submesoscale variability. Statistically significant spatial coherences exist for all products, with peaks close to 100 km. Based on these encouraging results, future research should focus on improving derivations of satellite-derived SSS in the Arctic Ocean and integrating model results to complement remote sensing observations. Text Arctic Arctic Ocean Sea ice Yukon river Yukon MDPI Open Access Publishing Arctic Arctic Ocean Yukon Carroll ENVELOPE(-81.183,-81.183,50.800,50.800) Remote Sensing 13 5 831 |
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Open Polar |
collection |
MDPI Open Access Publishing |
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language |
English |
topic |
sea surface salinity validation coastal |
spellingShingle |
sea surface salinity validation coastal Jorge Vazquez-Cuervo Chelle Gentemann Wenqing Tang Dustin Carroll Hong Zhang Dimitris Menemenlis Jose Gomez-Valdes Marouan Bouali Michael Steele Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models |
topic_facet |
sea surface salinity validation coastal |
description |
The Arctic Ocean is one of the most important and challenging regions to observe—it experiences the largest changes from climate warming, and at the same time is one of the most difficult to sample because of sea ice and extreme cold temperatures. Two NASA-sponsored deployments of the Saildrone vehicle provided a unique opportunity for validating sea-surface salinity (SSS) derived from three separate products that use data from the Soil Moisture Active Passive (SMAP) satellite. To examine possible issues in resolving mesoscale-to-submesoscale variability, comparisons were also made with two versions of the Estimating the Circulation and Climate of the Ocean (ECCO) model (Carroll, D; Menmenlis, D; Zhang, H.). The results indicate that the three SMAP products resolve the runoff signal associated with the Yukon River, with high correlation between SMAP products and Saildrone SSS. Spectral slopes, overall, replicate the −2.0 slopes associated with mesoscale-submesoscale variability. Statistically significant spatial coherences exist for all products, with peaks close to 100 km. Based on these encouraging results, future research should focus on improving derivations of satellite-derived SSS in the Arctic Ocean and integrating model results to complement remote sensing observations. |
format |
Text |
author |
Jorge Vazquez-Cuervo Chelle Gentemann Wenqing Tang Dustin Carroll Hong Zhang Dimitris Menemenlis Jose Gomez-Valdes Marouan Bouali Michael Steele |
author_facet |
Jorge Vazquez-Cuervo Chelle Gentemann Wenqing Tang Dustin Carroll Hong Zhang Dimitris Menemenlis Jose Gomez-Valdes Marouan Bouali Michael Steele |
author_sort |
Jorge Vazquez-Cuervo |
title |
Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models |
title_short |
Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models |
title_full |
Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models |
title_fullStr |
Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models |
title_full_unstemmed |
Using Saildrones to Validate Arctic Sea-Surface Salinity from the SMAP Satellite and from Ocean Models |
title_sort |
using saildrones to validate arctic sea-surface salinity from the smap satellite and from ocean models |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/rs13050831 |
op_coverage |
agris |
long_lat |
ENVELOPE(-81.183,-81.183,50.800,50.800) |
geographic |
Arctic Arctic Ocean Yukon Carroll |
geographic_facet |
Arctic Arctic Ocean Yukon Carroll |
genre |
Arctic Arctic Ocean Sea ice Yukon river Yukon |
genre_facet |
Arctic Arctic Ocean Sea ice Yukon river Yukon |
op_source |
Remote Sensing; Volume 13; Issue 5; Pages: 831 |
op_relation |
Ocean Remote Sensing https://dx.doi.org/10.3390/rs13050831 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/rs13050831 |
container_title |
Remote Sensing |
container_volume |
13 |
container_issue |
5 |
container_start_page |
831 |
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1774714425874317312 |