Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/162066
COMPARTIR / EXPORTAR:
logo share SHARE BASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE

Invitar a revisión por pares abierta
Título

Daily sea surface salinity variability in the tropical Pacific Ocean derived from satellite remote sensing data

AutorBallabrera-Poy, Joaquim CSIC ORCID ; Olmedo, Estrella CSIC ORCID ; Turiel, Antonio CSIC ORCID ; Portabella, Marcos CSIC ORCID ; Martínez, Justino CSIC ORCID ; Hoareau, Nina CSIC ORCID
Fecha de publicación18-abr-2016
EditorEuropean Geosciences Union
CitaciónGeophysical Research Abstracts 18: EGU2016-14651-4 (2016)
ResumenIn this work, a multifractal data fusion algorithm is used to obtain daily sea surface salinity (SSS) maps from the Soil Moisture and Ocean Salinity (SMOS) Level 2 (L2) data. The L2 SSS retrievals are obtained from the brightness temperature reconstructions at different polarizations and incidence angles along the satellite swath. SMOS L2 data have a spatial resolution of about 43 km and accuracy between 0.6 to 1.7 (in the practical salinity scale). The main goal of the data fusion algorithm is to use the reliable information of the OSTIA sea surface temperature (SST) daily fields to increase the spatial and temporal resolution of the SMOS L2 SSS data. Our SMOS dataset consists of the European Space Agency (ESA) L2 v620 reprocessed data from January 2010 to May 2015, and of the latest L2 operational data (near real-time) version after May 2015. Salinity anomalies are constructed by removing the five-year average of the L2 salinity data as a function of the geographical position, the overpass orientation (ascending or descending), and the across-track distance to the center of the swath. The SMOS-based climatologies evidence the existence of strong systematic artifacts, especially near the coast and, as such, they allow retrieving some of the systematic errors present in the original L2 data. The 0.05-degree, daily SST product from OSTIA is used as a template in our scalar fusion algorithm to generate 0.05 degree, daily SSS maps. The resulting SSS maps are less noisy and better define the main geophysical structures as compared to the standard high-level SSS products. Differences against near-surface Argo salinity measurements are reduced by 40% with respect to the standard products. In order to assess the significance of the extrapolation to the time domain, data from the Global Tropical Moored Buoy Array are used. The results indicate that the small time-scale variability present in the mooring data are not completely reproduced by remote sensing, although data fusion significantly increases the correlation between moored and remote sensing time variability
DescripciónEuropean Geosciences Union General Assembly 2016 (EGU2016), 17-22 April 2016, Vienna, Austria.-- 1 page
Versión del editorhttps://meetingorganizer.copernicus.org/EGU2016/EGU2016-14651-4.pdf
URIhttp://hdl.handle.net/10261/162066
Identificadoresissn: 1607-7962
Aparece en las colecciones: (ICM) Comunicaciones congresos




Ficheros en este ítem:
Fichero Descripción Tamaño Formato
Ballabrera_et_al_2016.pdf15,93 kBAdobe PDFVista previa
Visualizar/Abrir
Mostrar el registro completo

CORE Recommender

Page view(s)

179
checked on 18-mar-2024

Download(s)

67
checked on 18-mar-2024

Google ScholarTM

Check


NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.