Toward an Enhanced SMOS Level-2 Ocean Salinity Product
20 pages, 15 figures, 5 tables, .-- Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org).-- The Argo Program is part of the Global Ocean Observing System
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Language: | English |
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Institute of Electrical and Electronics Engineers
2020-10
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Subjects: | Debiased non-Bayesian, Latitudinal bias, Near real-time sea surface salinity (SSS) product, Nodal sampling, Soil Moisture and Ocean Salinity (SMOS), SSS, |
Online Access: | http://hdl.handle.net/10261/223214 http://dx.doi.org/10.13039/501100011033 |
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dig-icm-es-10261-2232142023-01-03T12:59:55Z Toward an Enhanced SMOS Level-2 Ocean Salinity Product Olmedo, Estrella González Gambau, Verónica Turiel, Antonio Guimbard, Sébastien González-Haro, Cristina Gabarró, Carolina Portabella, Marcos Arias, Manuel Sabia, Roberto Oliva, Roger Corbella, Ignasi Agencia Estatal de Investigación (España) Debiased non-Bayesian Latitudinal bias Near real-time sea surface salinity (SSS) product Nodal sampling Soil Moisture and Ocean Salinity (SMOS) SSS 20 pages, 15 figures, 5 tables, .-- Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org).-- The Argo Program is part of the Global Ocean Observing System The quality of the Soil Moisture and Ocean Salinity (SMOS) sea surface salinity (SSS) measurements has been noticeably improved in the past years. However, for some applications, there are still some limitations in the use of the Level-2 ocean salinity product. First, the SSS measurements are still affected by a latitudinal and seasonal bias. Second, the high standard deviation of the SSS error could significantly degrade part of the SSS signal. Finally, the coverage of the Level-2 salinity measurements is significantly reduced after applying filtering criteria to discard the poor-quality retrievals. In this work, we apply nodal sampling to the SMOS brightness temperatures (TBs), which effectively reduces the standard deviation of the TB error; then, we use debiased non-Bayesian retrieval for the mitigation of systematic biases on SSS and the statistical filtering criteria of the degraded salinity retrievals; and finally, we comprehensively characterize the residual latitudinal and seasonal biases and derive a correction for the retrieved SSS. We generate three years of an enhanced SMOS Level-2 Ocean Salinity product and we compare its performances with the ones corresponding to the European Space Agency SMOS Level-2 Ocean Salinity product (v662) With the funding support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI) Peer reviewed 2020-11-20T07:43:50Z 2020-11-20T07:43:50Z 2020-10 artículo http://purl.org/coar/resource_type/c_6501 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 6434-6453 (2020) 1939-1404 CEX2019-000928-S http://hdl.handle.net/10261/223214 10.1109/JSTARS.2020.3034432 2151-1535 http://dx.doi.org/10.13039/501100011033 en Publisher's version https://doi.org10.1109/JSTARS.2020.3034432 Sí open Institute of Electrical and Electronics Engineers |
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Debiased non-Bayesian Latitudinal bias Near real-time sea surface salinity (SSS) product Nodal sampling Soil Moisture and Ocean Salinity (SMOS) SSS Debiased non-Bayesian Latitudinal bias Near real-time sea surface salinity (SSS) product Nodal sampling Soil Moisture and Ocean Salinity (SMOS) SSS |
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Debiased non-Bayesian Latitudinal bias Near real-time sea surface salinity (SSS) product Nodal sampling Soil Moisture and Ocean Salinity (SMOS) SSS Debiased non-Bayesian Latitudinal bias Near real-time sea surface salinity (SSS) product Nodal sampling Soil Moisture and Ocean Salinity (SMOS) SSS Olmedo, Estrella González Gambau, Verónica Turiel, Antonio Guimbard, Sébastien González-Haro, Cristina Gabarró, Carolina Portabella, Marcos Arias, Manuel Sabia, Roberto Oliva, Roger Corbella, Ignasi Toward an Enhanced SMOS Level-2 Ocean Salinity Product |
description |
20 pages, 15 figures, 5 tables, .-- Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (https://argo.ucsd.edu, https://www.ocean-ops.org).-- The Argo Program is part of the Global Ocean Observing System |
author2 |
Agencia Estatal de Investigación (España) |
author_facet |
Agencia Estatal de Investigación (España) Olmedo, Estrella González Gambau, Verónica Turiel, Antonio Guimbard, Sébastien González-Haro, Cristina Gabarró, Carolina Portabella, Marcos Arias, Manuel Sabia, Roberto Oliva, Roger Corbella, Ignasi |
format |
artículo |
topic_facet |
Debiased non-Bayesian Latitudinal bias Near real-time sea surface salinity (SSS) product Nodal sampling Soil Moisture and Ocean Salinity (SMOS) SSS |
author |
Olmedo, Estrella González Gambau, Verónica Turiel, Antonio Guimbard, Sébastien González-Haro, Cristina Gabarró, Carolina Portabella, Marcos Arias, Manuel Sabia, Roberto Oliva, Roger Corbella, Ignasi |
author_sort |
Olmedo, Estrella |
title |
Toward an Enhanced SMOS Level-2 Ocean Salinity Product |
title_short |
Toward an Enhanced SMOS Level-2 Ocean Salinity Product |
title_full |
Toward an Enhanced SMOS Level-2 Ocean Salinity Product |
title_fullStr |
Toward an Enhanced SMOS Level-2 Ocean Salinity Product |
title_full_unstemmed |
Toward an Enhanced SMOS Level-2 Ocean Salinity Product |
title_sort |
toward an enhanced smos level-2 ocean salinity product |
publisher |
Institute of Electrical and Electronics Engineers |
publishDate |
2020-10 |
url |
http://hdl.handle.net/10261/223214 http://dx.doi.org/10.13039/501100011033 |
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