Colder and smaller : 10 years of observations of surface salinity by SMOS, Aquarius and SMAP to study mesoscale eddies in the Southern Ocean

2020 ◽  
Author(s):  
Audrey Hasson ◽  
Cori Pegliasco ◽  
Jacqueline Boutin ◽  
Rosemary Morrow
Keyword(s):  
Soil Moisture ◽  
Southern Ocean ◽  
European Space Agency ◽  
Mesoscale Eddy ◽  
Mesoscale Eddies ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
Sst Anomalies

<p>Since 2010, space missions dedicated to Sea Surface Salinity (SSS) have been providing observations with almost complete coverage of the global ocean and a resolution of about 45 km every 3 days. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission was the first orbiting radiometer to collect regular SSS observations from space. The Aquarius and SMAP (Soil Moisture Active-Passive) missions of the National Aeronautics and Space Administration (NASA) then reinforced the SSS observing system between mid-2011 and mid-2015 and since mid-2015, respectively.</p><p>Using the most recent SSS Climate Change Initiative project dataset merging data from the 3 missions, this study investigates the SSS signal associated with mesoscale eddies in the Southern Ocean. Eddies location and characteristics are obtained from the daily v3 mesoscale eddy trajectory atlas produced by CLS. SSS anomalies along the eddies journey are computed and compared to Sea Surface Temperature (SST) anomalies (v4 Remote Sensing Systems) as well as the SubAntarctic Front (SAF) position (CTOH, LEGOS). The vertical structure of the eddies is further investigated using profiles from colocated Argo autonomous floats.<span> </span></p><p>This study highlights a robust signal in SSS depending on both the eddies rotation (cyclone/anticyclone) and latitudinal position with respect to the SAF. Moreover, this dependence is not found in SST. These observations reveal oceanic the interaction of eddies with the larger scale ocean water masses. SSS and SST anomalies composites indeed show different patterns either bi-poles linked with horizontal stirring of fronts, mono-poles from trapping water or vertical mixing changes, or a mix of the two.</p><p>This analysis gives strong hints for the erosion of subsurface waters, such as mode waters, induced by enhanced mixing caused by the deep-reaching eddies of the southern ocean.</p>

scholarly journals The Role of Averaging for Improving Sea Surface Salinity Retrieval from the Soil Moisture and Ocean Salinity (SMOS) Satellite and Impact of Auxiliary Data

2007 ◽  
Vol 24 (2) ◽  
pp. 255-269 ◽  
Author(s):  
Sabine Philipps ◽  
Christine Boone ◽  
Estelle Obligis
Keyword(s):  
Soil Moisture ◽  
Sensitivity Study ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
Auxiliary Data ◽  
Additional Information ◽  
Ocean Salinity

Abstract Soil Moisture and Ocean Salinity (SMOS) was chosen as the European Space Agency’s second Earth Explorer Opportunity mission. One of the objectives is to retrieve sea surface salinity (SSS) from measured brightness temperatures (TBs) at L band with a precision of 0.2 practical salinity units (psu) with averages taken over 200 km by 200 km areas and 10 days [as suggested in the requirements of the Global Ocean Data Assimilation Experiment (GODAE)]. The retrieval is performed here by an inverse model and additional information of auxiliary SSS, sea surface temperature (SST), and wind speed (W). A sensitivity study is done to observe the influence of the TBs and auxiliary data on the SSS retrieval. The key role of TB and W accuracy on SSS retrieval is verified. Retrieval is then done over the Atlantic for two cases. In case A, auxiliary data are simulated from two model outputs by adding white noise. The more realistic case B uses independent databases for reference and auxiliary ocean parameters. For these cases, the RMS error of retrieved SSS on pixel scale is around 1 psu (1.2 for case B). Averaging over GODAE scales reduces the SSS error by a factor of 12 (4 for case B). The weaker error reduction in case B is most likely due to the correlation of errors in auxiliary data. This study shows that SSS retrieval will be very sensitive to errors on auxiliary data. Specific efforts should be devoted to improving the quality of auxiliary data.

Global validation of the ESA CCI+SSS products

2020 ◽  
Author(s):  
Adrien Martin ◽  
Sébastien Guimbard ◽  
Jacqueline Boutin ◽  
Nicolas Reul ◽  
Rafael Catany
Keyword(s):  
Soil Moisture ◽  
European Space Agency ◽  
Ground Truth ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
Spatial Gradients ◽  
Good Agreement

<p>The European Space Agency (ESA) Climate Change Initiative for Sea Surface Salinity (CCI+SSS) project aims at generating long-term, improved, calibrated global SSS fields from space. The project started in mid-2018 and in its first year has produced a 9-year dataset (2010-2018) from the three available L-band radiometer satellites (SMOS: Soil Moisture and Ocean Salinity; Aquarius; SMAP: Soil Moisture Active Passive) and validated it against in situ references (Argo and ISAS: In Situ Analysis System). The dataset is available at https://catalogue.ceda.ac.uk/uuid/9ef0ebf847564c2eabe62cac4899ec41.</p><p>The comparisons with in situ ground truth indicate much better performances than the ones obtained with a single satellite data product, with global precision against in situ references of 0.16 pss and 0.10 pss in areas with low variability. There is a very good agreement between the CCI dataset and references, including long-term stability, with differences within +-0.05 pss for global ocean within [40°S-20°N]. At higher latitude, we observe seasonal oscillation of the CCI SSS difference against references. The CCI SSS products uncertainty have been validated against references and show good agreement as long as the spatial representativeness is considered in presence of strong spatial gradients in salinity.</p>

scholarly journals Evaluation and Intercomparison of SMOS, Aquarius, and SMAP Sea Surface Salinity Products in the Arctic Ocean

2019 ◽  
Vol 11 (24) ◽  
pp. 3043 ◽  
Author(s):  
Séverine Fournier ◽  
Tong Lee ◽  
Wenqing Tang ◽  
Michael Steele ◽  
Estrella Olmedo
Keyword(s):  
Soil Moisture ◽  
Arctic Ocean ◽  
European Space Agency ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
The Arctic Ocean

Salinity is a critical parameter in the Arctic Ocean, having potential implications for climate and weather. This study presents the first systematic analysis of 6 commonly used sea surface salinity (SSS) products from the National Aeronautics and Space Administration (NASA) Aquarius and Soil Moisture Active Passive (SMAP) satellites and the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, in terms of their consistency among one another and with in-situ data. Overall, the satellite SSS products provide a similar characterization of the time mean SSS large-scale patterns and are relatively consistent in depicting the regions with strong SSS temporal variability. When averaged over the Arctic Ocean, the SSS show an excellent consistency in describing the seasonal and interannual variations. Comparison of satellite SSS with in-situ salinity measurements along ship transects suggest that satellite SSS captures salinity gradients away from regions with significant sea-ice concentration. The root-mean square differences (RMSD) of satellite SSS with respect to in-situ measurements improves with increasing temperature, reflecting the limitation of L-band radiometric sensitivity to SSS in cold water. However, the satellite SSS biases with respect to the in-situ measurements do not show a consistent dependence on temperature. The results have significant implications for the calibration and validation of satellite SSS as well as for the modeling community and the design of future satellite missions.

Validation of the ESA CCI+SSS products

2021 ◽  
Author(s):  
Adrien Martin ◽  
Sébastien Guimbard ◽  
Jacqueline Boutin ◽  
Nicolas Reul ◽  
Rafael Catany ◽  
...  
Keyword(s):  
Soil Moisture ◽  
European Space Agency ◽  
Ground Truth ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
Seasonal Oscillation ◽  
Good Agreement

<div> <p><span>The </span><span>European Space Agency (ESA) Climate Change Initiative (CCI+) for Sea Surface Salinity (CCI+SSS) project aims at generating long-term, improved, calibrated global SSS fields from space. </span>The project started in mid-2018 and in its second year (version 2) has produced a 10-year dataset (2010-2019) from the three available L-band radiometer satellites (SMOS: Soil Moisture and Ocean Salinity; Aquarius; SMAP: Soil Moisture Active Passive) and validated it against in situ references (Argo and ISAS: In Situ Analysis System). The comparisons with in situ ground truth indicate much better performances than the ones obtained with a single satellite data product, with global precision against in situ references of 0.15 pss. CCI SSS version 2 products show similar performance than version 1 but is one year longer. There is a very good agreement between the CCI dataset and references, including long-term stability, with differences within +-0.05 pss for global ocean within [40°S-20°N]. At higher latitude, we observe seasonal oscillation of the CCI SSS difference against references. The uncertainty provided in the CCI SSS product are in good agreement with observations (within +-25%).</p> </div>

scholarly journals Assessing Coastal SMAP Surface Salinity Accuracy and Its Application to Monitoring Gulf of Maine Circulation Dynamics

2018 ◽  
Vol 10 (8) ◽  
pp. 1232 ◽  
Author(s):  
Semyon Grodsky ◽  
Douglas Vandemark ◽  
Hui Feng
Keyword(s):  
Soil Moisture ◽  
Gulf Of Maine ◽  
Contamination Factor ◽  
Early Spring ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Sensor Sensitivity ◽  
Ocean Salinity ◽  
Circulation Dynamics

Monitoring the cold and productive waters of the Gulf of Maine and their interactions with the nearby northwestern (NW) Atlantic shelf is important but challenging. Although remotely sensed sea surface temperature (SST), ocean color, and sea level have become routine, much of the water exchange physics is reflected in salinity fields. The recent invention of satellite salinity sensors, including the Soil Moisture Active Passive (SMAP) radiometer, opens new prospects in regional shelf studies. However, local sea surface salinity (SSS) retrieval is challenging due to both cold SST limiting salinity sensor sensitivity and proximity to land. For the NW Atlantic, our analysis shows that SMAP SSS is subject to an SST-dependent bias that is negative and amplifies in winter and early spring due to the SST-related drop in SMAP sensor sensitivity. On top of that, SMAP SSS is subject to a land contamination bias. The latter bias becomes noticeable and negative when the antenna land contamination factor (LC) exceeds 0.2%, and attains maximum negative values at LC = 0.4%. Coastward of LC = 0.5%, a significant positive land contamination bias in absolute SMAP SSS is evident. SST and land contamination bias components are seasonally dependent due to seasonal changes in SST/winds and terrestrial microwave properties. Fortunately, it is shown that SSS anomalies computed relative to a satellite SSS climatology can effectively remove such seasonal biases along with the real seasonal cycle. SMAP monthly SSS anomalies have sufficient accuracy and applicability to extend nearer to the coasts. They are used to examine the Gulf of Maine water inflow, which displayed important water intrusions in between Georges Banks and Nova Scotia in the winters of 2016/17 and 2017/18. Water intrusion patterns observed by SMAP are generally consistent with independent measurements from the European Soil Moisture Ocean Salinity (SMOS) mission. Circulation dynamics related to the 2016/2017 period and enhanced wind-driven Scotian Shelf transport into the Gulf of Maine are discussed.

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

2019 ◽  
Vol 11 (15) ◽  
pp. 1818 ◽  
Author(s):  
Daniele Ciani ◽  
Rosalia Santoleri ◽  
Gian Luigi Liberti ◽  
Catherine Prigent ◽  
Craig Donlon ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Microwave Radiometer ◽  
Ocean Surface ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Optimal Interpolation ◽  
Surface Salinity ◽  
Processing Chain

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.

Validation and correction of sea surface salinity retrieval from SMAP

2020 ◽  
Author(s):  
Sisi Qin
Keyword(s):  
Soil Moisture ◽  
In Situ Measurements ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Tropical Regions ◽  
Ocean Salinity ◽  
Forecast Models ◽  
Level 3

<p>In this study, Sea Surface Salinity (SSS) Level 3 (L3) daily product derived from Soil Moisture Active Passive (SMAP) during the year 2016, was validated and compared with SSS daily products derived from Soil Moisture and Ocean Salinity (SMOS) and in-situ measurements. Generally, the Root Mean Square Error (RMSE) of the daily SSS products is larger along the coastal areas and at high latitudes and is smaller in the tropical regions and open oceans. Comparisons between the two types of daily satellite SSS product revealed that the RMSE was higher in the daily SMOS product than in the SMAP, whereas the bias of the daily SMOS was observed to be less than that of the SMAP when compared with Argo floats data. In addition, the latitude-dependent bias and RMSE of the SMAP SSS were found to be primarily influenced by the precipitation and the Sea Surface Temperature (SST).Then, aregression analysis method which has adopted the precipitation and SST data was used to correct the larger bias of the daily SMAP product. It was confirmed that the corrected daily SMAP product could be used for assimilation in high-resolution forecast models, due to the fact that it was demonstrated to be unbiased and much closer to the in-situ measurements than the original uncorrected SMAP product.</p>

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>

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