Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis

Olmedo, Estrella; Taupier‐Letage, Isabelle; Turiel, Antonio; Alvera‐Azcárate, Aida

doi:10.3390/rs10030485

Cited by 40 publications

(52 citation statements)

References 41 publications

(54 reference statements)

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“…ABACUS in situ observations were compared to co-located L3 and L4 maps of SMOS SSS, recently obtained at the BEC through the methodology developed by Reference [27]. This methodology takes advantage of a combination of the new retrieval debiased non-Bayesian algorithm, DINEOF, and multifractal fusion to retrieve enhanced SSS fields over the North Atlantic Ocean and the Mediterranean Sea.…”

Section: Smos L3 and L4 Sea Surface Salinity Productsmentioning

confidence: 99%

“…Nevertheless, evident biases were found when analyzing SSS values in comparison with Argo floats. To overcome these problems, a new set of SMOS SSS enhanced products [27] has been obtained at the Barcelona Expert Center (BEC) through a combination of debiased non-Bayesian retrieval [28], deletion of time-dependent residual biases by means of DINEOF (data interpolating empirical orthogonal functions) [29], and multifractal fusion with high resolution sea surface temperature (OSTIA SST) maps [30].…”

Section: Introductionmentioning

confidence: 99%

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In Situ and Satellite Sea Surface Salinity in the Algerian Basin Observed through ABACUS Glider Measurements and BEC SMOS Regional Products

et al. 2019

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The Algerian Basin is a key area for the general circulation in the western Mediterranean Sea. The basin has an intense inflow/outflow regime with complex circulation patterns, involving both fresh Atlantic water and more saline Mediterranean water. Several studies have demonstrated the advantages of the combined use of autonomous underwater vehicles, such as gliders, with remotely sensed products (e.g., altimetry, MUR SST) to observe meso- and submesoscale structures and their properties. An important contribution could come from a new generation of enhanced satellite sea surface salinity (SSS) products, e.g., those provided by the Soil Moisture and Ocean Salinity (SMOS) mission. In this paper, we assess the advantages of using Barcelona Expert Center (BEC) SMOS SSS products, obtained through a combination of debiased non-Bayesian retrieval, DINEOF (data interpolating empirical orthogonal functions) and multifractal fusion with high resolution sea surface temperature (OSTIA SST) maps. Such an aim was reached by comparing SMOS Level-3 (L3) and Level-4 (L4) SSS products with in situ high resolution glider measurements collected in the framework of the Algerian Basin Circulation Unmanned Survey (ABACUS) observational program conducted in the Algerian Basin during falls 2014–2016. Results show that different levels of confidence between in situ and satellite measurements can be achieved according to the spatial scales of variability. Although SMOS values slightly underestimate in situ observations (mean difference is −0.14 (−0.11)), with a standard deviation of 0.25 (0.26) for L3 (L4) products), at basin scale, the enhanced SMOS products well represent the salinity patterns described by the ABACUS data.

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Section: Smos L3 and L4 Sea Surface Salinity Productsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In Situ and Satellite Sea Surface Salinity in the Algerian Basin Observed through ABACUS Glider Measurements and BEC SMOS Regional Products

et al. 2019

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“…The three-dimensional ISAS dataset was recently improved using quasi-global ARGO floats observations [6]. In addition, the characterization of the sea-surface salinity (SSS) has benefited from satellite observations and on their synergy with in situ measurements, e.g., [7][8][9].…”

Section: Introductionmentioning

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%

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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“…Empirical Orthogonal Function (EOF) based interpolation is an other category widely used in geosciences [11][12][13]. It relies on a Singular Value Decomposition (SVD) to compute an EOF basis, the field is then reconstructed by projecting the observations on the EOF subspace until a convergence criterion is reached [14].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature

et al. 2018

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The forecasting and reconstruction of oceanic dynamics is a crucial challenge. While model driven strategies are still the state-of-the-art approaches in the reconstruction of spatio-temporal dynamics. The ever increasing availability of data collections in oceanography raised the relevance of data-driven approaches as computationally efficient representations of spatio-temporal fields reconstruction. This tools proved to outperform classical state-of-the-art interpolation techniques such as optimal interpolation and DINEOF in the retrievement of fine scale structures while still been computationally efficient comparing to model based data assimilation schemes. However, coupling this data-driven priors to classical filtering schemes limits their potential representativity. From this point of view, the recent advances in machine learning and especially neural networks and deep learning can provide a new infrastructure for dynamical modeling and interpolation within a data-driven framework. In this work we adress this challenge and develop a novel Neural-Network-based (NN-based) Kalman filter for spatio-temporal interpolation of sea surface dynamics. Based on a data-driven probabilistic representation of spatio-temporal fields, our approach can be regarded as an alternative to classical filtering schemes such as the ensemble Kalman filters (EnKF) in data assimilation. Overall, the key features of the proposed approach are two-fold: (i) we propose a novel architecture for the stochastic representation of two dimensional (2D) geophysical dynamics based on a neural networks, (ii) we derive the associated parametric Kalman-like filtering scheme for a computationally-efficient spatio-temporal interpolation of Sea Surface Temperature (SST) fields. We illustrate the relevance of our contribution for an OSSE (Observing System Simulation Experiment) in a case-study region off South Africa. Our numerical experiments report significant improvements in terms of reconstruction performance compared with operational and state-of-the-art schemes (e.g., optimal interpolation, Empirical Orthogonal Function (EOF) based interpolation and analog data assimilation).

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Improving SMOS Sea Surface Salinity in the Western Mediterranean Sea through Multivariate and Multifractal Analysis

Cited by 40 publications

References 41 publications

In Situ and Satellite Sea Surface Salinity in the Algerian Basin Observed through ABACUS Glider Measurements and BEC SMOS Regional Products

In Situ and Satellite Sea Surface Salinity in the Algerian Basin Observed through ABACUS Glider Measurements and BEC SMOS Regional Products

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

Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature

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