The emission and scattering of L‐band microwave radiation from rough ocean surfaces and wind speed measurements from the Aquarius sensor

Meißner, Thomas; Wentz, F. J.; Ricciardulli, Lucrezia

doi:10.1002/2014jc009837

Cited by 97 publications

(107 citation statements)

References 67 publications

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“…The MLE minimizes the sum of square (SOS) difference between the Aquarius measurements of T B0 of the two polarizations (vertical V and horizontal H) and the values of the radiative transfer model calculation for T B0 . This model calculation which is based on the dielectric constant of sea-water [10][11][12]. Figure 1 shows a schematic flow diagram of the salinity retrieval algorithm.…”

Section: Basic Algorithm Flowmentioning

confidence: 99%

“…SST is a crucial input to the dielectric constant model [10][11][12], which enters the computation of the emissivity of a flat ocean surface. An evaluation was made of the performance of the SSS retrieval algorithm with various ancillary SST fields [13].…”

Section: Sea Surface Temperature (Sst)mentioning

confidence: 99%

“…The NCEP GDAS 1-deg, 6-h scalar wind speed field is used as background field in the Aquarius wind speed retrievals [12]. It is available from http://nomads.ncep.noaa.gov/.…”

Section: Wind Speed Background Fieldmentioning

confidence: 99%

“…The correction of the wind induced surface roughness and the model for the wind induced emissivity in the Aquarius salinity retrieval algorithm is based on the results of [12]. In Version 5 a few changes were implemented.…”

Section: Surface Roughness Correctionmentioning

confidence: 99%

“…The purpose for these changes was the mitigation of biases that have been found in earlier releases and that could be traced back to the surface emissivity model. The major change from [12] concerns the SST dependence of the wind emissivity ∆E W . The functional form ρ(T S ) of this SST dependence in Version 5, which is shown in Figure 2, is given as follows:…”

Section: Surface Roughness Correctionmentioning

confidence: 99%

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The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases

2018

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Abstract:The Aquarius end-of-mission (Version 5) salinity data set was released in December 2017. This article gives a comprehensive overview of the main steps of the Level 2 salinity retrieval algorithm. In particular, we will discuss the corrections for wind induced surface roughness, atmospheric oxygen absorption, reflected galactic radiation and side-lobe intrusion from land surfaces. Most of these corrections have undergone major updates from previous versions, which has helped mitigating temporal and zonal biases. Our article also discusses the ocean target calibration for Aquarius Version 5. We show how formal error estimates for the Aquarius retrievals can be obtained by perturbing the input to the algorithm. The performance of the Aquarius Version 5 salinity retrievals is evaluated against salinity measurements from the ARGO network and the HYCOM model. When stratified as function of sea surface temperature or sea surface wind speed, the difference between Aquarius Version 5 and ARGO is within ±0.1 psu. The estimated global RMS uncertainty for monthly 100 km averages is 0.128 psu for the Aquarius Version 5 retrievals. Finally, we show how the Aquarius Version 5 salinity retrieval algorithm is adapted to retrieve salinity from the Soil-Moisture Active Passion (SMAP) mission.

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Section: Basic Algorithm Flowmentioning

confidence: 99%

Section: Sea Surface Temperature (Sst)mentioning

confidence: 99%

“…The NCEP GDAS 1-deg, 6-h scalar wind speed field is used as background field in the Aquarius wind speed retrievals [12]. It is available from http://nomads.ncep.noaa.gov/.…”

Section: Wind Speed Background Fieldmentioning

confidence: 99%

Section: Surface Roughness Correctionmentioning

confidence: 99%

Section: Surface Roughness Correctionmentioning

confidence: 99%

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The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases

2018

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Surface roughness and breaking wave properties retrieved from polarimetric microwave radar backscattering

Hwang

Fois

2015

JGR Oceans

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Ocean surface roughness and wave breaking are the two main contributors of radar backscattering from the ocean surface. The relative weightings of the two contributions vary with the microwave polarization: the VV (vertical transmit vertical receive) is dominated by the Bragg resonance scattering mechanism, and the HH (horizontal transmit horizontal receive) and VH (horizontal transmit vertical receive or vertical transmit horizontal receive) contain nontrivial non‐Bragg contributions mainly produced by breaking features. A method is developed to obtain the short‐scale properties of ocean surface roughness and wave breaking from Ku, C, and L band polarimetric sea returns. The results are used for quantitative evaluation of the ocean surface roughness spectral models and for deriving understanding of the breaking contribution important to microwave ocean remote sensing, in particular its dependence on wind speed, microwave frequency, and incidence angle. Implications of the results to air‐sea interaction applications are discussed.

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A joint active/passive physical model of sea surface microwave signatures

Plant

Irisov²

2017

JGR Oceans

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Active and passive microwave signatures of the ocean can only depend on the ocean wave spectrum if bound and breaking waves are neglected. However, history has not been kind to attempts to explain both radiometer brightness temperatures (Tb) and normalized radar cross sections (σo) of the sea using the same ocean wave spectrum without bound and breaking waves. In this paper, we show that if bound and breaking waves are included in physical models of radiometer and scatterometer microwave signatures of the ocean, a single wave spectrum can explain both Tb and σo to reasonable accuracy. Bound waves are the roughness produced by gently breaking, or crumpling, waves that travel near the speed of the parent wave. Bound wave modeling is based on earlier work by Plant (1997) but using additional information about the slope probability distributions of the bound waves' parents. Breaking wave and foam modeling both build on the function Λ(cb) introduced by Phillips (1985), which describes the average length of breaking wavefronts on the ocean per unit area as a function of breaker velocity, cb. We model Λ(cb) as documented in Irisov and Plant (2016) where we show that the wave spectrum completely determines Λ(cb). We thus show here that the result of including bound and breaking waves in radiometer and scatterometer models of oceanic signatures is a much closer fit to data over a wide range of microwave frequencies and incidence angles using a single wave spectrum.

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The emission and scattering of L‐band microwave radiation from rough ocean surfaces and wind speed measurements from the Aquarius sensor

Cited by 97 publications

References 67 publications

The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases

The Salinity Retrieval Algorithms for the NASA Aquarius Version 5 and SMAP Version 3 Releases

Surface roughness and breaking wave properties retrieved from polarimetric microwave radar backscattering

A joint active/passive physical model of sea surface microwave signatures

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