Accurate measurements of the dielectric constant of seawater at L band

Lang, Roger H.; Zhou, Yiwen; Utku, C.; Vine, David Le

doi:10.1002/2015rs005776

Cited by 66 publications

(36 citation statements)

References 37 publications

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“…It is therefore critical to improve the current dielectric constant models. Recent laboratory measurements performed at L-band at the George Washington University (GWU) [57] have led to a new model that shows promising results with the Aquarius data [54]. These measurements are currently being enhanced by adding more samples at more SST and SSS.…”

Section: Discussionmentioning

confidence: 99%

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

et al. 2019

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Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration’s (NASA’s) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from the Argo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS. We also identify the impact of the corrections for atmospheric effects recently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

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Section: Discussionmentioning

confidence: 99%

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

et al. 2019

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“…First, satellite SSS retrieval in high latitude seawater is complicated by several factors. L-band instrument sensitivity to SSS is greatly reduced in cold seawater [39,40]. Leakage of emissivity from sea ice into the satellite antenna's main lobe or through the antenna side-lobes can contaminate salinity signals if undetected [41].…”

Section: Introductionmentioning

confidence: 99%

The Potential and Challenges of Using Soil Moisture Active Passive (SMAP) Sea Surface Salinity to Monitor Arctic Ocean Freshwater Changes

et al. 2018

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Sea surface salinity (SSS) links various components of the Arctic freshwater system. SSS responds to freshwater inputs from river discharge, sea ice change, precipitation and evaporation, and oceanic transport through the open straits of the Pacific and Atlantic oceans. However, in situ SSS data in the Arctic Ocean are very sparse and insufficient to depict the large-scale variability to address the critical question of how climate variability and change affect the Arctic Ocean freshwater. The L-band microwave radiometer on board the NASA Soil Moisture Active Passive (SMAP) mission has been providing SSS measurements since April 2015, at approximately 60 km resolution with Arctic Ocean coverage in 1-2 days. With improved land/ice correction, the SMAP SSS algorithm that was developed at the Jet Propulsion Laboratory (JPL) is able to retrieve SSS in ice-free regions 35 km of the coast. SMAP observes a large-scale contrast in salinity between the Atlantic and Pacific sides of the Arctic Ocean, while retrievals within the Arctic Circle vary over time, depending on the sea ice coverage and river runoff. We assess the accuracy of SMAP SSS through comparative analysis with in situ salinity data collected by Argo floats, ships, gliders, and in field campaigns. Results derived from nearly 20,000 pairs of SMAP and in situ data North of 50 • N collocated within a 12.5-km radius and daily time window indicate a Root Mean Square Difference (RMSD) less thañ 1 psu with a correlation coefficient of 0.82 and a near unity regression slope over the entire range of salinity. In contrast, the Hybrid Coordinate Ocean Model (HYCOM) has a smaller RMSD with Argo. However, there are clear systematic biases in the HYCOM for salinity in the range of 25-30 psu, leading to a regression slope of about 0.5. In the region North of 65 • N, the number of collocated samples drops more than 70%, resulting in an RMSD of about 1.2 psu. SMAP SSS in the Kara Sea shows a consistent response to discharge anomalies from the Ob' and Yenisei rivers between 2015 and 2016, providing an assessment of runoff impact in a region where no in situ salinity data are available for validation. The Kara Sea SSS anomaly observed by SMAP is missing in the HYCOM SSS, which assimilates climatological runoffs without interannual changes. We explored the feasibility of using SMAP SSS to monitor the sea surface salinity variability at the major Arctic Ocean gateways. Results show that although the SMAP SSS is limited to about 1 psu accuracy, many large salinity changes are observable. This may lead to the potential application of satellite SSS in the Arctic monitoring system as a proxy of the upper ocean layer freshwater exchanges with subarctic oceans.

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“…SST and SSS dependent uncertainties in the Meissner-Wentz dielectric model will be assessed by the team. This will be based on comparing the real and imaginary parts of the permittivity model as well as the flat surface emission with the results of the laboratory measurement at W-band (Guillou et al 1998), which are regarded as very reliable, as well as with the recent L-band laboratory measurements by Lang et al (2016).…”

Section: Next Stepsmentioning

confidence: 99%

Reference-Quality Emission and Backscatter Modeling for the Ocean

English

Prigent

Johnson

et al. 2020

Bulletin of the American Meteorological Society

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(ISSI) met to discuss the challenge of developing a community reference-quality ocean emission and reflection model for use across a broad spectral range [microwave (MW) and infrared (IR), and possibly also visible] as well as supporting passive and active remote sensing. The need for this has been identified in various reports and international workshops. Notably, the European Commission Horizon2020 project, GAIA-CLIM 1 (see appendix for acronyms), identified that the lack of a reference-quality ocean emission and backscatter model was a major gap in our ability to provide absolute calibration of the satellite based observing system. The gap was also identified by the ECMWF-JCSDA-NWP SAF all-sky assimilation workshop in December 2015 and again in February 2020 and the twenty-first meeting of the International TOVS Working Group in December 2017. An international science team was proposed to ISSI, and accepted, to develop a new model capability with these goals and characteristics:

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Accurate measurements of the dielectric constant of seawater at L band

Cited by 66 publications

References 37 publications

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

The Potential and Challenges of Using Soil Moisture Active Passive (SMAP) Sea Surface Salinity to Monitor Arctic Ocean Freshwater Changes

Reference-Quality Emission and Backscatter Modeling for the Ocean

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