Nine years of SMOS sea surface salinity global maps at the Barcelona Expert Center

Olmedo, Estrella; González‐Haro, Cristina; Hoareau, Nina; Umbert, Marta; González-Gambau, Verónica; Martínez, Justino; Gabarró, Carolina; Turiel, Antonio

doi:10.5194/essd-13-857-2021

Cited by 38 publications

(52 citation statements)

References 44 publications

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“…That is, the real satellite observation includes all the errors associated with converting raw brightness temperatures into a value of SSS, roughness corrections, galactic noise, etc. [2,3,30] The simulated values do not contain any of that, only representation error as discussed above. For that reason, we wanted to see what impact adding noise to the input data would do to the computed RMSD.…”

Section: Resultsmentioning

confidence: 96%

“…These missions utilize sun-synchronous polar orbits with high inclinations, but differing spatial and temporal resolutions, and have provided continuous SSS measurement coverage of the global ocean [1]. They all measure ocean brightness temperature at 1.4 GHz (L-band) which can be converted to SSS, a process known as retrieval [2,3]. The specifications for each of the missions is nicely summarized by [4] (their Figure 2).…”

Section: Introductionmentioning

confidence: 99%

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Matchup Characteristics of Sea Surface Salinity using a High-resolution Ocean Model

Bingham¹,

Fournier²,

Brodnitz³

et al. 2022

Preprint

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Sea surface salinity (SSS) satellite measurements are validated using in situ observations 8 usually made by surfacing Argo floats. Validation statistics are computed using matched values of 9 SSS from satellites and floats. This study explores how the matchup process is done using a high- 10 resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the 11 Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped 12 up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference 13 (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values 14 being greater than satellites. RMSD is computed using an “all salinity difference” method that av- 15 erages level 2 satellite observations within a given time and space window for comparison with 16 Argo floats. RMSD values range from 0.08 to 0.18 depending on the space-time window and the 17 satellite. This range gives an estimate of the representation error inherent in comparing single point 18 Argo floats to area-average satellite values. The study has implications for future SSS satellite mis- 19 sions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS 20 values.

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Section: Resultsmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Matchup Characteristics of Sea Surface Salinity using a High-resolution Ocean Model

Bingham¹,

Fournier²,

Brodnitz³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The main purpose of this paper is to get estimates of SFV to include in error budgets for satellites. SFV itself is small and mostly insignificant relative to other sources of error [4,6,41]. At 100 km (40 km) footprint size, typical annual median values of SFV are about 0.02-0.15 (0.02-0.07) .…”

Section: Discussionmentioning

confidence: 97%

Sea surface salinity subfootprint variability from a global high-resolution model

Bingham¹,

Brodnitz²,

Fournier³

et al. 2022

Preprint

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Subfootprint variability (SFV) is variability at a spatial scale smaller than the footprint of a sat-ellite, and 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 is to estimate the SFV for sea surface salinity (SSS) satellite observations. This is done using a high-resolution (1/48°) numerical model, the MITgcm, from which one year of output has recently become availa-ble. SFV, defined as the weighted standard deviation of SSS within the satellite footprint, was computed from the model for a 2°X2° grid of points for the one model year. We present maps of SFV for 40 and 100 km footprint size, display histograms of its distribution for a range of foot-print sizes and quantify its seasonality. At 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 bound-ary and eastern equatorial regions, and a few other areas. SFV has strong variability throughout the year, with generally largest values in the fall season. We also quantify 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 satellite obser-vation of SSS.

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“…The same CTC methodology can be applied to the retrieved geophysical parameters (both soil moisture and ocean salinity). We are currently working in the error characterization of different L-band sea surface salinity products (including SMAP, SMOS, and reanalysis data) with promising results [38].…”

Section: Discussionmentioning

confidence: 99%

Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land

et al. 2020

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The error characterization of satellite observations is crucial for blending observations from multiple platforms into a unique dataset and for assimilating them into numerical weather prediction models. In the last years, the triple collocation (TC) technique has been widely used to assess the quality of many geophysical variables acquired with different instruments and at different scales. This paper presents a new formulation of the triple collocation (Correlated Triple Collocation (CTC)) for the case of three datasets that resolve similar spatial scales, with two of them being error-correlated datasets. Besides, the formulation is designed to ensure fast convergence of the error estimators. This approach is of special interest in cases such that finding more than three datasets with uncorrelated errors is not possible and the amount of data is limited. First, a synthetic experiment has been carried out to assess the performance of CTC formulation. As an example of application, the error characterization of three collocated L-band brightness temperature (TB) measurements over land has been performed. Two of the datasets come from ESA (European Space Agency) SMOS (Soil Moisture and Ocean Salinity) mission: one is the reconstructed TB from the operational L1B v620 product, and the other is the reconstructed TB from the operational L1B v620 product resulting from application of an RFI (Radio Frequency Interference) mitigation technique, the nodal sampling (NS). The third is an independent dataset, the TB acquired by a NASA (National Aeronautics and Space Administration) SMAP (Soil Moisture Active Passive) radiometer. Our analysis shows that the application of NS leads to TB error reduction with respect to the current version of SMOS TB in 80% of the points in the global map, with an average reduction of approximately 1 K over RFI-free regions and approximately 1.45 K over strongly RFI-contaminated areas.

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Nine years of SMOS sea surface salinity global maps at the Barcelona Expert Center

Cited by 38 publications

References 44 publications

Matchup Characteristics of Sea Surface Salinity using a High-resolution Ocean Model

Matchup Characteristics of Sea Surface Salinity using a High-resolution Ocean Model

Sea surface salinity subfootprint variability from a global high-resolution model

Triple Collocation Analysis for Two Error-Correlated Datasets: Application to L-Band Brightness Temperatures over Land

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