A model function for the ocean‐normalized radar cross section at 14 GHz derived from NSCAT observations

Wentz, F. J.; Smith, Derrick W.

doi:10.1029/98jc02148

Cited by 230 publications

(150 citation statements)

References 7 publications

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“…In the literature, from microwave (C and K u bands for instance) experimental data [28][29][30][31][32][33][34], the backscattering NRCS (BNRCS) can be expressed for pq = {V V , H H } co-polarizations in the form…”

Section: Bnrcsmentioning

confidence: 99%

“…For the co-polarizations, this allows us to obtain closed-form expressions of the Fourier coefficients {σ n } of the backscattering NRCS σ in the form σ = σ 0 + σ 1 cos φ + σ 2 cos(2φ), which is consistent with microwave measurements [28][29][30][31][32][33][34]. The angle φ is the observation azimuthal angle with respect to the wind direction, and the coefficients {σ n } depend on the observation elevation angle θ and on the wind speed.…”

Section: Introductionmentioning

confidence: 95%

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Numerical implementation of local unified models for backscattering from random rough sea surfaces

Bourlier

Pinel

2009

Waves in Random and Complex Media

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In the context of electromagnetic wave backscattering from ocean-like surfaces, by using the lowest order of the SSA (SSA-1) model, Bourlier et al. proposed an original technique to reduce the number of numerical integrations to two for easier numerical implementation. To be consistent with microwave measurements, closed-form expressions of the Fourier coefficients with respect to the wind direction of the backscattering normalized radar cross-section (NRCS) are obtained. For Gaussian statistics, previous work is extended in this paper to kernels of unified models expanded up to second order, like full SSA and full LCA. Thus, with the help of Bessel functions and by analytical integrations over the azimuthal angles, the secondorder backscattering (BNRCS) is expressed in terms of two-fold integrations and another independent integration instead of four-fold integrations, if no analytical integration is made. This approach allows us to obtain fast results (less than one second). Numerical results are then presented for different microwave frequencies and wind speeds.

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Section: Bnrcsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Numerical implementation of local unified models for backscattering from random rough sea surfaces

Bourlier

Pinel

2009

Waves in Random and Complex Media

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“…and Dunbar 1993, Wentz and Smith 1999). This process uses a geophysical model function and applies each Wave Vector Cell (WVC) dictated by sensors.…”

Section: Quikscat Scatterometermentioning

confidence: 99%

“…SEASAT, the first satellite to detect sea-surface wind vectors, was launched in 1978 but unfortunately operated for only 3 months. Several scatterometer sensors for seawind detection were launched after SEASAT: ERS (European Remote Sensing satellite)-1, ERS-2, NASA (National Aeronautics and Space Administration) NSCAT, and QuikSCAT in 1991, 1995, 1996, and 1999, respectively (Hoffman and Leidner 2005.…”

Section: Introductionmentioning

confidence: 99%

Satellite‐based assessment of the impact of sea‐surface winds on regional atmospheric circulations over the Korean Peninsula

Lee

Kim

Lee

2008

International Journal of Remote Sensing

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In order to clarify the impact of sea-wind satellite-data assimilation under fair and calm weather conditions over the Korean Peninsula, several numerical experiments were carried out. The numerical model used in this study was the Fifth Mesoscale Model (MM5), verified by many previous studies; satellite data of sea winds were gathered by the QuikSCAT sensor launched on ADEOS II. During the daytime, the change in mesoscale circulations according to the assimilation of sea-wind data increased in the coastal area and decreased deep inland. Although variations in mesoscale circulations due to satellite-based seawind data assimilation occurred near the coastal area, the mixing height far inland over the Korean Peninsula often changed according to the intensity of inflow from the surrounding seas and that of the topographic effect. In this study, a lower mixing height inland was generated by a numerical simulation using QuikSCAT sea-wind data assimilation. The lower mixing height was caused by strong westerly and weak easterly winds from the Yellow Sea and the East Sea, respectively. The validation of the sea-wind influence was verified quantitatively by RMSD and IOA analyses. The accuracy of weather forecasting increased at the East Sea and Korea Strait and decreased at the Yellow Sea. These different accuracy levels were caused by the inhomogeneity of sea-wind data quality. The maintenance of the uniform quality of satellite-based data on sea wind is an important factor in the reasonable forecasting of mesoscale circulations.

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“…The combination of the L, C and Ku bands provide a good coverage of the gravity-capillary wave spectrum for the wavenumber ranging from 25 to 500 rad/m. The following derivation is based on L-band GMF [18], C-band CMOD5 GMF [19] and Ku-band NSCAT2 GMF [20]. The L-band GMF and CMOD5 GMF express the NRCSs as second-order cosine harmonic functions of the radar-observed azimuthal angle with the analytical functions [21].…”

Section: Data Descriptionmentioning

confidence: 99%

Directional Spreading Function of the Gravity-Capillary Wave Spectrum Derived from Radar Observations

Zhou

Chong

et al. 2017

Remote Sensing

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Directional spreading function of the gravity-capillary wave spectrum can provide the high-wavenumber wave energy distribution among different directions on the sea surface. The existing directional spreading functions have been mainly developed for the low-wavenumber gravity wave with buoy data. In this paper, we use radar observations to derive the directional spreading function of the gravity-capillary wave spectrum, which is expressed as the second-order Fourier series expansion. So far the standard form of the second-order harmonic coefficient has not been proposed to correctly unify the gravity and gravity-capillary wave. Our strategy is to introduce a correcting term to replace the inaccurate gravity-capillary spectral component in Elfouhaily's directional spreading function. The second-order harmonic coefficient at L, C and Ku band calculated by the radar observation is used to fit the correcting term to obtain one at the full gravity-capillary wave region. According to our proposed the directional spreading function, there is a spectral region between the gravity and gravity-capillary range where it signifies the negative upwind-crosswind asymmetry at low and moderate speed range. And this is not reflected by the previous models, but has been confirmed by radar observations. The Root Mean Square Difference (RMSD) of the proposed second-order harmonic coefficient versus the radar-observed one at L, C band Ku band is 0.0438, 0.0263 and 0.0382, respectively. The overall bias and RMSD are −0.0029 and 0.0433 for the whole second-order harmonic coefficient range, respectively. The result verifies the accuracy of the proposed directional spreading function at L, C band Ku band.

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A model function for the ocean‐normalized radar cross section at 14 GHz derived from NSCAT observations

Cited by 230 publications

References 7 publications

Numerical implementation of local unified models for backscattering from random rough sea surfaces

Numerical implementation of local unified models for backscattering from random rough sea surfaces

Satellite‐based assessment of the impact of sea‐surface winds on regional atmospheric circulations over the Korean Peninsula

Directional Spreading Function of the Gravity-Capillary Wave Spectrum Derived from Radar Observations

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