scholarly journals Analyzing the 2010-2011 La Niña signature in the tropical Pacific sea surface salinity using in situ data, SMOS observations, and a numerical simulation

2014 ◽  
Vol 119 (6) ◽  
pp. 3855-3867 ◽  
Author(s):  
Audrey Hasson ◽  
Thierry Delcroix ◽  
Jacqueline Boutin ◽  
Raphael Dussin ◽  
Joaquim Ballabrera-Poy
Keyword(s):  
Numerical Simulation ◽  
Tropical Pacific ◽  
La Niña ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
In Situ Data ◽  
La Nina ◽  
The Tropical Pacific

Isolation of sea surface salinity maps on various timescales in the tropical Pacific Ocean

2012 ◽  
Vol 68 (5) ◽  
pp. 687-701 ◽  
Author(s):  
Jian Chen ◽  
Ren Zhang ◽  
Huizan Wang ◽  
Yuzhu An ◽  
Peng Peng ◽  
...  
Keyword(s):  
Pacific Ocean ◽  
Tropical Pacific ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Tropical Pacific Ocean ◽  
Surface Salinity ◽  
The Tropical Pacific

High-resolution sea surface salinity and temperature in coastal areas from Sentinel-2 and Copernicus Marine in situ data

2020 ◽  
Author(s):  
Encarni Medina-Lopez
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
In Situ Data ◽  
The Neural Network ◽  
Sentinel 2

<p>The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m x 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82% and 84% for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 C and 0.4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.</p>

Precipitation and sea-surface salinity in the tropical Pacific Ocean

1996 ◽  
Vol 43 (7) ◽  
pp. 1123-1141 ◽  
Author(s):  
Thierry Delcroix ◽  
Christian Henin ◽  
Véronique Porte ◽  
Phillip Arkin
Keyword(s):  
Pacific Ocean ◽  
Tropical Pacific ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Tropical Pacific Ocean ◽  
Surface Salinity ◽  
The Tropical Pacific

scholarly journals Seven Years of SMOS Sea Surface Salinity at High Latitudes: Variability in Arctic and Sub-Arctic Regions

2018 ◽  
Vol 10 (11) ◽  
pp. 1772 ◽  
Author(s):  
Estrella Olmedo ◽  
Carolina Gabarró ◽  
Verónica González-Gambau ◽  
Justino Martínez ◽  
Joaquim Ballabrera-Poy ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Remotely Sensed ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
In Situ Data ◽  
Arctic Regions ◽  
The Difference

This paper aims to present and assess the quality of seven years (2011–2017) of 25 km nine-day Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) objectively analyzed maps in the Arctic and sub-Arctic oceans ( 50 ∘ N– 90 ∘ N). The SMOS SSS maps presented in this work are an improved version of the preliminary three-year dataset generated and freely distributed by the Barcelona Expert Center. In this new version, a time-dependent bias correction has been applied to mitigate the seasonal bias that affected the previous SSS maps. An extensive database of in situ data (Argo floats and thermosalinograph measurements) has been used for assessing the accuracy of this product. The standard deviation of the difference between the new SMOS SSS maps and Argo SSS ranges from 0.25 and 0.35. The major features of the inter-annual SSS variations observed by the thermosalinographs are also captured by the SMOS SSS maps. However, the validation in some regions of the Arctic Ocean has not been feasible because of the lack of in situ data. In those regions, qualitative comparisons with SSS provided by models and the remotely sensed SSS provided by Aquarius and SMAP have been performed. Despite the differences between SMOS and SMAP, both datasets show consistent SSS variations with respect to the model and the river discharge in situ data, but present a larger dynamic range than that of the model. This result suggests that, in those regions, the use of the remotely sensed SSS may help to improve the models.

Pi-MEP Salinity – an ESA-NASA Platform for sustained satellite surface salinity validation 

2021 ◽  
Author(s):  
Roberto Sabia ◽  
Sebastien Guimbard ◽  
Nicolas Reul ◽  
Tony Lee ◽  
Julian Schanze ◽  
...  
Keyword(s):  
Program Planning ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
In Situ Data ◽  
Sea Level Anomalies ◽  
Ocean Salinity ◽  
Salinity Data ◽  
Planning Group

<p>The Pilot Mission Exploitation Platform (Pi-MEP) for Salinity (www.salinity-pimep.org) has been released operationally in 2019 to the broad oceanographic community, in order to foster satellite sea surface salinity validation and exploitation activities.</p><p>Specifically, the Platform aims at enhancing salinityvalidation, by allowing systematic inter-comparison of various EO datasets with a broad suite of in-situ data, and also at enabling oceanographic process studies by capitalizing on salinity data in synergy with additional spaceborne estimates.</p><p> </p><p>Despite Pi-MEP was originally conceived as an ESA initiative to widen the uptake of the Soil Moisture and Ocean Salinity (SMOS) mission data over ocean, a project partnership with NASA was devised soon after the operational deployment, and an official collaboration endorsed within the ESA-NASA Joint Program Planning Group (JPPG).</p><p> </p><p>The Salinity Pi-MEP has therefore become a reference hub for SMOS, SMAP and Aquarius satellite salinity missions, which are assessed in synergy with additional thematic datasets (e.g., precipitation, evaporation, currents, sea level anomalies, ocean color, sea surface temperature). </p><p>Match-up databases of satellite/in situ (such as Argo, TSG, moorings, drifters) data and corresponding validation reports at different spatiotemporal scales are systematically generated; furthermore, recently-developed dedicated tools allow data visualization, metrics computation and user-driven features extractions.</p><p> </p><p>The Platform is also meant to monitor salinity in selected oceanographic “case studies”, ranging from river plumes monitoring to SSS characterization in challenging regions, such as high latitudes or semi-enclosed basins.</p><p> </p><p>The two Agencies are currently collaborating to widen the Platform features on several technical aspects - ranging from a triple-collocation software implementation to a sustained exploitation of data from the SPURS-1/2 campaigns. In this context, an upgrade of the satellite/in-situ match-up methodology has been recently agreed, resulting into a redefinition of the validation criteria that will be subsequently implemented in the Platform.</p><p> </p><p>A further synthesis of the three satellites salinity algorithms, models and auxiliary data handling is at the core of the ESA Climate Change Initiative (CCI) on Salinity and of ESA-NASA further collaboration.</p>

scholarly journals Spatial Scales of Sea Surface Salinity Subfootprint Variability in the SPURS Regions

2020 ◽  
Vol 12 (23) ◽  
pp. 3996
Author(s):  
Frederick M. Bingham ◽  
Zhijin Li
Keyword(s):  
North Atlantic ◽  
Spatial Scales ◽  
Remote Sensing Data ◽  
Ocean Modeling ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Regional Ocean Modeling System ◽  
Surface Salinity ◽  
In Situ Data

Subfootprint variability (SFV), or representativeness error, is variability within the footprint of a satellite that can impact validation by comparison of in situ and remote sensing data. This study seeks to determine the size of the sea surface salinity (SSS) SFV as a function of footprint size in two regions that were heavily sampled with in situ data. The Salinity Processes in the Upper-ocean Regional Studies-1 (SPURS-1) experiment was conducted in the subtropical North Atlantic in the period 2012–2013, whereas the SPURS-2 study was conducted in the tropical eastern North Pacific in the period 2016–2017. SSS SFV was also computed using a high-resolution regional model based on the Regional Ocean Modeling System (ROMS). We computed SFV at footprint sizes ranging from 20 to 100 km for both regions. SFV is strongly seasonal, but for different reasons in the two regions. In the SPURS-1 region, the meso- and submesoscale variability seemed to control the size of the SFV. In the SPURS-2 region, the SFV is much larger than SPURS-1 and controlled by patchy rainfall.

scholarly journals Subfootprint Variability of Sea Surface Salinity Observed during the SPURS-1 and SPURS-2 Field Campaigns

2019 ◽  
Vol 11 (22) ◽  
pp. 2689 ◽  
Author(s):  
Frederick M. Bingham
Keyword(s):  
Absolute Error ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Satellite Measurement ◽  
Surface Salinity ◽  
Passive Measurement ◽  
In Situ Data ◽  
Field Campaigns ◽  
Comparison Of The Results

Subfootprint variability (SFV), variability within the footprint of a satellite measurement, is a source of error associated with the validation process, especially for a satellite measurement with a large footprint such as those measuring sea surface salinity (SSS). This type of error has not been adequately quantified in the past. In this study, I have examined SFV using in situ ocean data from the SPURS-1 (Salinity Processes in the Upper ocean Regional Studies-1) and SPURS-2 field campaigns in the subtropical North Atlantic and eastern tropical North Pacific respectively. I computed SFV from these data over two one-year periods of intense sampling. The results show that SFV is highly seasonal. I have computed SFV errors in several different forms, a median value of the weekly snapshot error, a total snapshot error, an absolute error of the Aquarius and SMAP (Soil Moisture Active Passive) measurement, a part of that error associated with SFV and a bias due to the skewness of the distribution of SSS. These results are characteristic only of the particular regions studied. However, comparison of the results with high resolution models, and in situ data from moorings gives the possibility of getting global estimates of SFV from these other more common sources of SSS data.

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