Sea Surface Salinity Subfootprint Variability from a Global High-Resolution Model

Subfootprint variability (SFV) is variability at a spatial scale smaller than the footprint of a satellite, and it cannot be resolved by satellite observations. It is important to quantify and understand, as it contributes to the error budget for satellite data. The purpose of this study was to esti...

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Autores principales:	Frederick M. Bingham, Susannah Brodnitz, Severine Fournier, Karly Ulfsax, Akiko Hayashi, Hong Zhang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	sea surface salinity subfootprint variability errors validation Science Q
Acceso en línea:	https://doaj.org/article/20c7fd5e5f1f4921a1914c17f01195c5
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id	oai:doaj.org-article:20c7fd5e5f1f4921a1914c17f01195c5
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spelling	oai:doaj.org-article:20c7fd5e5f1f4921a1914c17f01195c52021-11-11T18:55:52ZSea Surface Salinity Subfootprint Variability from a Global High-Resolution Model10.3390/rs132144102072-4292https://doaj.org/article/20c7fd5e5f1f4921a1914c17f01195c52021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/21/4410https://doaj.org/toc/2072-4292Subfootprint variability (SFV) is variability at a spatial scale smaller than the footprint of a satellite, and it cannot be resolved by satellite observations. It is important to quantify and understand, as it contributes to the error budget for satellite data. The purpose of this study was to estimate the SFV for sea surface salinity (SSS) satellite observations. This was performed by using a high-resolution numerical model, a 1/48° version of the MITgcm simulation, from which one year of output has recently become available. SFV, defined as the weighted standard deviation of SSS within the satellite footprint, was computed from the model for a 2° × 2° grid of points for the one model year. We present maps of median SFV for 40 and 100 km footprint size, display histograms of its distribution for a range of footprint sizes and quantify its seasonality. At a 100 km (40 km) footprint size, SFV has a mode of 0.06 (0.04). It is found to vary strongly by location and season. It has larger values in western-boundary and eastern-equatorial regions, as well as in a few other areas. SFV has strong variability throughout the year, with the largest values generally being in the fall season. We also quantified the representation error, the degree of mismatch between random samples within a footprint and the footprint average. Our estimates of SFV and representation error can be used in understanding errors in the satellite observation of SSS.Frederick M. BinghamSusannah BrodnitzSeverine FournierKarly UlfsaxAkiko HayashiHong ZhangMDPI AGarticlesea surface salinitysubfootprint variabilityerrorsvalidationScienceQENRemote Sensing, Vol 13, Iss 4410, p 4410 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sea surface salinity subfootprint variability errors validation Science Q
spellingShingle	sea surface salinity subfootprint variability errors validation Science Q Frederick M. Bingham Susannah Brodnitz Severine Fournier Karly Ulfsax Akiko Hayashi Hong Zhang Sea Surface Salinity Subfootprint Variability from a Global High-Resolution Model
description	Subfootprint variability (SFV) is variability at a spatial scale smaller than the footprint of a satellite, and it cannot be resolved by satellite observations. It is important to quantify and understand, as it contributes to the error budget for satellite data. The purpose of this study was to estimate the SFV for sea surface salinity (SSS) satellite observations. This was performed by using a high-resolution numerical model, a 1/48° version of the MITgcm simulation, from which one year of output has recently become available. SFV, defined as the weighted standard deviation of SSS within the satellite footprint, was computed from the model for a 2° × 2° grid of points for the one model year. We present maps of median SFV for 40 and 100 km footprint size, display histograms of its distribution for a range of footprint sizes and quantify its seasonality. At a 100 km (40 km) footprint size, SFV has a mode of 0.06 (0.04). It is found to vary strongly by location and season. It has larger values in western-boundary and eastern-equatorial regions, as well as in a few other areas. SFV has strong variability throughout the year, with the largest values generally being in the fall season. We also quantified the representation error, the degree of mismatch between random samples within a footprint and the footprint average. Our estimates of SFV and representation error can be used in understanding errors in the satellite observation of SSS.
format	article
author	Frederick M. Bingham Susannah Brodnitz Severine Fournier Karly Ulfsax Akiko Hayashi Hong Zhang
author_facet	Frederick M. Bingham Susannah Brodnitz Severine Fournier Karly Ulfsax Akiko Hayashi Hong Zhang
author_sort	Frederick M. Bingham
title	Sea Surface Salinity Subfootprint Variability from a Global High-Resolution Model
title_short	Sea Surface Salinity Subfootprint Variability from a Global High-Resolution Model
title_full	Sea Surface Salinity Subfootprint Variability from a Global High-Resolution Model
title_fullStr	Sea Surface Salinity Subfootprint Variability from a Global High-Resolution Model
title_full_unstemmed	Sea Surface Salinity Subfootprint Variability from a Global High-Resolution Model
title_sort	sea surface salinity subfootprint variability from a global high-resolution model
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/20c7fd5e5f1f4921a1914c17f01195c5
work_keys_str_mv	AT frederickmbingham seasurfacesalinitysubfootprintvariabilityfromaglobalhighresolutionmodel AT susannahbrodnitz seasurfacesalinitysubfootprintvariabilityfromaglobalhighresolutionmodel AT severinefournier seasurfacesalinitysubfootprintvariabilityfromaglobalhighresolutionmodel AT karlyulfsax seasurfacesalinitysubfootprintvariabilityfromaglobalhighresolutionmodel AT akikohayashi seasurfacesalinitysubfootprintvariabilityfromaglobalhighresolutionmodel AT hongzhang seasurfacesalinitysubfootprintvariabilityfromaglobalhighresolutionmodel
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Sea Surface Salinity Subfootprint Variability from a Global High-Resolution Model

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