Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data

Nardelli, Bruno Buongiorno; Droghei, Riccardo; Santoleri, Rosalia

doi:10.1016/j.rse.2015.12.052

Cited by 38 publications

(22 citation statements)

References 38 publications

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“…There are other SSS products from SMOS but comparing and analyzing them all would be impractical and is beyond the scope of the present paper. It is worth mentioning, however, that because the Level‐2 SMOS SSS retrievals at ∼40 km resolution contain a high level of noise (∼0.6–1.7 psu) [ Reul et al ., ], averaging or smoothing over a relatively large spatial and temporal window is typically required to reduce the error to an acceptable level; therefore, the effective resolution of gridded SSS products can be much lower [ Kolodziejczyk et al ., ; Buongiorno Nardelli et al ., ].…”

Section: Methodsmentioning

confidence: 99%

Signature of mesoscale eddies in satellite sea surface salinity data

Melnichenko

Amores

Maximenko

et al. 2017

J. Geophys. Res. Oceans

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A persistent signature of coherent mesoscale eddies in sea surface salinity (SSS) is revealed by analyzing the relationship between satellite SSS and sea surface height (SSH) variability in an eddy‐following reference frame. Our analysis focuses on mid‐ocean eddies in two representative regions, the southern Indian Ocean and the North Atlantic subtropical gyre. The resulting composite averages reveal a clear signature of mesoscale eddies in satellite SSS with typical SSS anomalies of 0.03–0.05 psu. The spatial structure of eddy‐induced SSS perturbations can be characterized as a superposition of a dipole structure, arising from horizontal advection of the background SSS gradient by eddy velocity field, and a monopole structure related to the eddy core. The observed relationships between SSS and SSH anomalies are used to provide a regional assessment of the role of mesoscale eddies in the ocean freshwater transport in the North Atlantic subtropical gyre.

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Section: Methodsmentioning

confidence: 99%

Signature of mesoscale eddies in satellite sea surface salinity data

Melnichenko

Amores

Maximenko

et al. 2017

J. Geophys. Res. Oceans

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“…Blended sea surface temperature (SST) (Reynolds et al, ) distributed through the National Climatic Data Centre (http://www.ncdc.noaa.gov/oisst); delayed time optimally interpolated daily maps of Absolute Dynamic Topography (ADT) from Jason‐1, Jason‐2, Envisat, Cryosat2, and HY‐2 altimeter data (AVISO+, ), distributed by CMEMS (SEALEVEL_GLO_PHY_L4_REP_OBSERVATIONS_008_047: http://marine.copernicus.eu/documents/QUID/CMEMS-SL-QUID-008-032-051.pdf); daily (one per week) sea surface salinity (SSS) maps developed and distributed in the framework of the European Space Agency “Ocean ecoSystem Modelling based on Observations from Satellite and In‐Situ data” (OSMOSIS) project (http://osmosis.artov.isac.cnr.it/products/). The latter were obtained through a multidimensional interpolation of satellite salinity data acquired by Soil Moisture Ocean Salinity (SMOS) and in situ SSS measurements (Buongiorno Nardelli et al, ; Droghei et al, ). It must be stressed that this algorithm is effectively able to retrieve high‐resolution SSS patterns by including high‐pass filtered high‐resolution SST information in the covariance function used to weight SSS observations.…”

Section: Datamentioning

confidence: 99%

Three‐Dimensional Ageostrophic Motion and Water Mass Subduction in the Southern Ocean

2018

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Vertical velocities at the ocean mesoscale are several orders of magnitude smaller than corresponding horizontal flows, making their direct monitoring a still unsolved challenge. Vertical motion is generally retrieved indirectly by applying diagnostic equations to observation‐based fields. The most common approach relies on the solution of an adiabatic version of the Omega equation, neglecting the ageostrophic secondary circulation driven by frictional effects and turbulent mixing in the boundary layers. Here we apply a diabatic semigeostrophic diagnostic model to two different 3‐D reconstructions covering the Southern Ocean during the period 2010–2012. We incorporate the effect of vertical mixing through a modified K‐profile parameterization and using ERA‐interim data, and perform an indirect validation of the ageostrophic circulation with independent drifter observations. Even if horizontal gradients and associated vertical flow are likely underestimated at 1/4° × 1/4° resolution, the exercise provides an unprecedented relative quantification of the contribution of vertical mixing and adiabatic internal dynamics on the vertical exchanges along the Antarctic Circumpolar Current. Kinematic estimates of subduction rates show the destruction of poleward flowing waters lighter than 26.6 kg/m3 (14 ÷ 15 Sv) and two main positive bands associated with the Antarctic Intermediate Water (7 ÷ 11 Sv) and Sub‐Antarctic Mode Waters (4 ÷ 7 Sv) formation, while Circumpolar Deep Water upwelling attains around 3 ÷ 6 Sv. Diabatic and adiabatic terms force distinct spatial responses and vertical velocity magnitudes along the water column and the restratifying effect of adiabatic internal dynamics due to mesoscale eddies is shown to at least partly compensate the contribution of wind‐driven vertical exchanges to net subduction.

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“…and provide guidance to the OI system when interpolating SSS fields resulting in an enhanced effective resolution compared to simple space-time interpolation approaches. The technique, originally developed to interpolate in situ SSS, was successively adapted to ingest satellite observations from SMOS and finally calibrated to compute dynamically consistent SSS/SSD datasets [10,11,18]. The theoretical framework of the SSS multivariate OI is briefly illustrated below, referring the reader to [10,11,15,18] for more details.…”

Section: Processing Chain Descriptionmentioning

confidence: 99%

“…In Equation (1), C is the background error covariance matrix and R, assumed diagonal, is the observations error covariance matrix (here defined by constant values per each observation type/platform). In the implementation described by [10,11], the background field is provided by an analysis built from in situ observations alone through a classical OI. More recently, in the framework of CMEMS, the background field estimate was modified by computing a first round of space-time OI based on in situ input data relying on a monthly climatology.…”

Section: Processing Chain Descriptionmentioning

confidence: 99%

“…In the framework of the Copernicus Marine Environment Monitoring Service (CMEMS), a daily, mesoscale resolving SSS multi-year gap-free Level-4 analysis (L4 hereinafter) product was developed by Buongiorno Nardelli et al 2016 [10] and Drogheiet al 2018 [11]. The approach of [10,11], unlike [5][6][7][8][9], relies on a multi-dimensional (multivariate) optimal interpolation (OI) algorithm that combines both Soil Moisture Ocean Salinity (SMOS) satellite retrievals and in situ salinity measurements with satellite sea-surface temperature information. This product is distributed operationally in near real time in late 2018.…”

Section: Introductionmentioning

confidence: 99%

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Copernicus Imaging Microwave Radiometer (CIMR) Benefits for the Copernicus Level 4 Sea-Surface Salinity Processing Chain

et al. 2019

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We present a study on the potential of the Copernicus Imaging Microwave Radiometer (CIMR) mission for the global monitoring of Sea-Surface Salinity (SSS) using Level-4 (gap-free) analysis processing. Space-based SSS are currently provided by the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellites. However, there are no planned missions to guarantee continuity in the remote SSS measurements for the near future. The CIMR mission is in a preparatory phase with an expected launch in 2026. CIMR is focused on the provision of global coverage, high resolution sea-surface temperature (SST), SSS and sea-ice concentration observations. In this paper, we evaluate the mission impact within the Copernicus Marine Environment Monitoring Service (CMEMS) SSS processing chain. The CMEMS SSS operational products are based on a combination of in situ and satellite (SMOS) SSS and high-resolution SST information through a multivariate optimal interpolation. We demonstrate the potential of CIMR within the CMEMS SSS operational production after the SMOS era. For this purpose, we implemented an Observing System Simulation Experiment (OSSE) based on the CMEMS MERCATOR global operational model. The MERCATOR SSSs were used to generate synthetic in situ and CIMR SSS and, at the same time, they provided a reference gap-free SSS field. Using the optimal interpolation algorithm, we demonstrated that the combined use of in situ and CIMR observations improves the global SSS retrieval compared to a processing where only in situ observations are ingested. The improvements are observed in the 60% and 70% of the global ocean surface for the reconstruction of the SSS and of the SSS spatial gradients, respectively. Moreover, the study highlights the CIMR-based salinity patterns are more accurate both in the open ocean and in coastal areas. We conclude that CIMR can guarantee continuity for accurate monitoring of the ocean surface salinity from space.

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Multi-dimensional interpolation of SMOS sea surface salinity with surface temperature and in situ salinity data

Cited by 38 publications

References 38 publications

Signature of mesoscale eddies in satellite sea surface salinity data

Signature of mesoscale eddies in satellite sea surface salinity data

Three‐Dimensional Ageostrophic Motion and Water Mass Subduction in the Southern Ocean

Copernicus Imaging Microwave Radiometer (CIMR) Benefits for the Copernicus Level 4 Sea-Surface Salinity Processing Chain

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