Comparative Analysis between Sea Surface Salinity Derived from SMOS Satellite Retrievals and in Situ Measurements

Liu, Yuyao; Han, Kaifeng; Bao, Senliang; Chen, Wen; Ren, Kaijun

doi:10.3390/rs14215465

Cited by 2 publications

(3 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The ISAS database has been used for SL, temperature/heat content and salinity studies in many oceanic regions (Germineaud et al., 2023; Hasson et al., 2014; Hernandez et al., 2014; Kolodziejczyk et al., 2019; Llovel et al., 2022; H. Wang et al., 2022) and has shown similar performance to other commonly used databases such as the Roemmich–Gilson Argo climatology (e.g., Germineaud et al., 2023). In addition to the T and S analyzed fields, the ISAS20 provides the analyzed variance and the normalized analyzed variance, the so‐called PCTVAR (percentage of the a priori variance), which basically depends on the data sampling, on the correlation scales and on the measurement errors.…”

Section: Methodsmentioning

confidence: 99%

“…The ISAS database has been used for SL, temperature/heat content and salinity studies in many oceanic regions (Germineaud et al, 2023;Hasson et al, 2014;Hernandez et al, 2014;Kolodziejczyk et al, 2019;Llovel et al, 2022;H. Wang et al, 2022) and has shown similar performance to other commonly used databases such as the Roemmich-Gilson Argo climatology (e.g., Germineaud et al, 2023).…”

Section: Temperature and Salinity Datamentioning

confidence: 99%

See 1 more Smart Citation

Loop Current Eddies as a Possible Cause of the Rapid Sea Level Rise in the Gulf of Mexico

Thirion,

Birol,

Jouanno

2024

JGR Oceans

View full text Add to dashboard Cite

The Gulf of Mexico (GOM), with its densely populated coastline, is one of the world's most vulnerable regions to climate change and sea level (SL) rise. Over the last three decades, various works have been conducted to assess coastal SL trends around the basin using tide gauge stations, separately from studies dealing with regional dynamical processes. Using altimetry, Argo, and eddy atlas products over the period from January 1993 to December 2020, we have analyzed the regional SL variations in the area to define their characteristics and explore their possible dynamical causes. We observe a mean GIA‐corrected SL rise rate of 4.81 ± 0.85 mm yr−1, which is 25% higher than that of the adjacent Caribbean Sea and 44% higher than that of the global ocean. This result highlights the singular SL evolution in the GOM over the 28‐year study period. Over 2010–2020, the SL trend in the GOM has even accelerated, along with a strong warming of the upper‐layer (0.58 ± 0.17°C), which explains ∼60% of the SL rise rate through the thermosteric effect. Finally, the heat input estimates emphasize the role of the Loop Current eddies as a major contributor to the recent acceleration of SL rise due to upper‐layer warming.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Temperature and Salinity Datamentioning

confidence: 99%

Loop Current Eddies as a Possible Cause of the Rapid Sea Level Rise in the Gulf of Mexico

Thirion,

Birol,

Jouanno

2024

JGR Oceans

View full text Add to dashboard Cite

show abstract

“…Where RMSE and MAE are the Root Mean Square Error and the Mean Absolute Error [59,60], and their calculation equations are as follows:…”

Section: Deep Neural Network Modelmentioning

confidence: 99%

The SSR Brightness Temperature Increment Model Based on a Deep Neural Network

Wen,

Zhang,

Shu

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The SSS (sea surface salinity) is an important factor affecting global climate changes, sea dynamic environments, global water cycles, marine ecological environments, and ocean carbon cycles. Satellite remote sensing is a practical way to observe SSS from space, and the key to retrieving SSS satellite products is to establish an accurate sea surface brightness temperature forward model. However, the calculation results of different forward models, which are composed of different relative permittivity models and SSR (sea surface roughness) brightness temperature increment models, are different, and the impact of this calculation difference has exceeded the accuracy requirement of the SSS inversion, and the existing SSR brightness temperature increment models, which primarily include empirical models and theoretical models, cannot match all the relative permittivity models. In order to address this problem, this paper proposes a universal DNN (deep neural network) model architecture and corresponding training scheme, and provides different SSR brightness temperature increment models for different relative permittivity models utilizing DNN based on offshore experiment data, and compares them with the existing models. The results show that the DNN models perform significantly better than the existing models, and that their calculation accuracy is close to the detection accuracy of a radiometer. Therefore, this study effectively solves the problem of SSR brightness temperature correction under different relative permittivity models, and provides a theoretical support for high-precision SSS inversion research.

show abstract

Comparative Analysis between Sea Surface Salinity Derived from SMOS Satellite Retrievals and in Situ Measurements

Cited by 2 publications

References 67 publications

Loop Current Eddies as a Possible Cause of the Rapid Sea Level Rise in the Gulf of Mexico

Loop Current Eddies as a Possible Cause of the Rapid Sea Level Rise in the Gulf of Mexico

The SSR Brightness Temperature Increment Model Based on a Deep Neural Network

Contact Info

Product

Resources

About