scholarly journals High-Frequency Variations in Pearl River Plume Observed by Soil Moisture Active Passive Sea Surface Salinity

2020 ◽  
Vol 12 (3) ◽  
pp. 563 ◽  
Author(s):  
Xiaomei Liao ◽  
Yan Du ◽  
Tianyu Wang ◽  
Shuibo Hu ◽  
Haigang Zhan ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Frequency ◽  
River Plume ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Pearl River ◽  
Surface Salinity ◽  
The Pearl River ◽  
Offshore Transport ◽  
Frequency Variations

River plumes play an important role in the cross-margin transport of phytoplankton and nutrients, which have profound impacts on coastal ecosystems. Using recently available Soil Moisture Active Passive (SMAP) sea surface salinity (SSS) data and high-resolution ocean color products, this study investigated summertime high-frequency variations in the Pearl River plume of China and its biological response. The SMAP SSS captures the intraseasonal oscillations in the offshore transport of the Pearl River plume well, which has distinct 30–60 day variations from mid-May to late September. The offshore transport of freshwater varies concurrently with southwesterly wind anomalies and is roughly in phase with the Madden–Julian Oscillation (MJO) index in phases 1–5, thus implying that the MJO exerts a significant influence. During MJO phases 1–2, the southwest wind anomalies in the northeastern South China Sea (SCS) enhanced cross-shore Ekman transport, while the northeast wind anomalies during MJO phases 3–5 favored the subsequent southwestward transport of the plume. The high chlorophyll-a concentration coincided well with the low-salinity water variations, emphasizing the important role of the offshore transport of the Pearl River plume in sustaining biological production over the oligotrophic northern SCS. The strong offshore transport of the plume in June 2015 clearly revealed that the proximity of a cyclonic eddy plays a role in the plume’s dispersal pathway. In addition, heavy rainfall related to the landfall of tropical cyclones in the Pearl River Estuary region contributed to the episodic offshore transport of the plume.

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.

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.

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>

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>

scholarly journals Sea Surface Salinity with SMOS

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Mulyadi Abdul Wahid
Keyword(s):  
Soil Moisture ◽  
Microwave Radiometer ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Normal Operation ◽  
Surface Salinity ◽  
Processing Centre ◽  
Ocean Salinity ◽  
Long Time ◽  
A New Technique

The mission to observe the Sea Surface Salinity (SSS) from the space is not really new because it has been started from long time ago. The first mission was the Skylab which used a 1.4 GHz microwave radiometer in 1970’s. But this mission is still not as comprehensive as other missions which observe such as Sea Surface Temperature (SST), Sea Surface Height (SSH), Ocean Color, and so on. Realizing the importance of SSS distribution in the ocean and its influences to the Earth’s climate system has motivated the scientists to develop a new technique in observing the SSS from space and lead a mission called the SMOS mission which was launched in November 2, 2011. Besides observing the SSS, this mission observes the Soil Moisture as well. The Soil Moisture and Ocean Salinity (SMOS) mission aims to obtain global and regular measurements on the soil moisture and the ocean salinity. These measurements are essential for climate and hydrological models, among other purposes. SMOS payload is a L band (21 cm, 1.4 GHz) 2D interferometric radiometer on a generic Proteus platform. The mission lifetime is at least 3 years (0.5 for commissioning and 2.5 for normal operation) + 2 years (extended operation) + 10 years for the post-mission processing. Raw physical data, level 1 and level 2 products will be produced by the PDPC (SMOS Payload Data and Processing Centre). It is an ESA center located in Villafranca (Spain) and operated under the responsibility of ESA. The SMOS Ocean Salinity objective is accuracy better than 0.1 psu, with 10 days to monthly grid scale (200 km).

scholarly journals Intercomparison of Salinity Products in the Beaufort Gyre and Arctic Ocean

2021 ◽  
Vol 14 (1) ◽  
pp. 71
Author(s):  
Sarah B. Hall ◽  
Bulusu Subrahmanyam ◽  
James H. Morison
Keyword(s):  
Soil Moisture ◽  
Arctic Ocean ◽  
Satellite Observations ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
The Arctic ◽  
Surface Salinity ◽  
Beaufort Gyre ◽  
The Arctic Ocean

Salinity is the primary determinant of the Arctic Ocean’s density structure. Freshwater accumulation and distribution in the Arctic Ocean have varied significantly in recent decades and certainly in the Beaufort Gyre (BG). In this study, we analyze salinity variations in the BG region between 2012 and 2017. We use in situ salinity observations from the Seasonal Ice Zone Reconnaissance Surveys (SIZRS), CTD casts from the Beaufort Gyre Exploration Project (BGP), and the EN4 data to validate and compare with satellite observations from Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Aquarius Optimally Interpolated Sea Surface Salinity (OISSS), and Arctic Ocean models: ECCO, MIZMAS, HYCOM, ORAS5, and GLORYS12. Overall, satellite observations are restricted to ice-free regions in the BG area, and models tend to overestimate sea surface salinity (SSS). Freshwater Content (FWC), an important component of the BG, is computed for EN4 and most models. ORAS5 provides the strongest positive SSS correlation coefficient (0.612) and lowest bias to in situ observations compared to the other products. ORAS5 subsurface salinity and FWC compare well with the EN4 data. Discrepancies between models and SIZRS data are highest in GLORYS12 and ECCO. These comparisons identify dissimilarities between salinity products and extend challenges to observations applicable to other areas of the Arctic Ocean.

scholarly journals Improving Australian Rainfall Prediction Using Sea Surface Salinity

2021 ◽  
pp. 1-56
Author(s):  
Saurabh Rathore ◽  
Nathaniel L. Bindoff ◽  
Caroline C. Ummenhofer ◽  
Helen E. Phillips ◽  
Ming Feng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Random Forest ◽  
Indian Ocean ◽  
Indian Ocean Dipole ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Random Forest Regression ◽  
Dry Conditions ◽  
Australian Rainfall

AbstractThis study uses sea surface salinity (SSS) as an additional precursor for improving the prediction of summer (December-February, DJF) rainfall over northeastern Australia. From a singular value decomposition between SSS of prior seasons and DJF rainfall, we note that SSS of the Indo-Pacific warm pool region [SSSP (150°E-165°W and 10°S-10°N), and SSSI (50°E-95°E and 10°S-10°N)] co-varies with Australian rainfall, particularly in the northeast region. Composite analysis based on high (low) SSS events in SSSP and SSSI region is performed to understand the physical links between the SSS and the atmospheric 31 moisture originating from the regions of anomalously high (low) SSS and precipitation over Australia. The composites show the signature of co-occurring La Niña and negative Indian Ocean dipole (co-occurring El Niño and positive Indian Ocean dipole) with anomalously wet (dry) conditions over Australia. During the high (low) SSS events of SSSP and SSSI regions, the convergence (divergence) of incoming moisture flux results in anomalously wet (dry) conditions over Australia with a positive (negative) soil moisture anomaly. We show from the random forest regression analysis that the local soil moisture, El Niño Southern Oscillation (ENSO) and SSSP are the most important precursors for the northeast Australian rainfall whereas, for the Brisbane region ENSO, SSSP and Indian Ocean Dipole (IOD) are the most important. The prediction of Australian rainfall using random forest regression shows an improvement by including SSS from the prior season. This evidence suggests that sustained observations of SSS can improve the monitoring of the Australian regional hydrological cycle.

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