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

Hwang, Paul A.; Fois, F.

doi:10.1002/2015jc010782

Cited by 86 publications

(81 citation statements)

References 46 publications

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“…However, as illustrated by the Chl a patterns in Figure 1d, obtained by Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, 20 h after the SAR image in Figure 1a, the Chl a concentration is very low, corresponding to the low backscatter region shown in Figure 1b. Typically, the divergence of surface currents modulates the ocean wave properties, like local MSS and wave breaking parameters, and thus affects the ocean surface roughness detected by SAR measurements [e.g., Alpers, 1985;Lyzenga and Bennett, 1988;Hwang et al, 2013;Hwang and Fois, 2015]. Typically, the divergence of surface currents modulates the ocean wave properties, like local MSS and wave breaking parameters, and thus affects the ocean surface roughness detected by SAR measurements [e.g., Alpers, 1985;Lyzenga and Bennett, 1988;Hwang et al, 2013;Hwang and Fois, 2015].…”

Section: Physical Mechanism For Sar Low Backscattersmentioning

confidence: 99%

“…In addition, wave breaking of large-scale waves also alters the surface roughness and generates smaller-scale Bragg waves [e.g., Johannessen et al, 2005;Hwang and Fois, 2015]. Specifically, the surface roughness is partitioned into one part that relates to large-scale waves and a second part that relates to small-scale Bragg waves.…”

Section: Physical Mechanism For Sar Low Backscattersmentioning

confidence: 99%

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Radar imaging of intense nonlinear Ekman divergence

Liu

Perrie

Kudryavtsev

et al. 2016

Geophysical Research Letters

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In general, given an oceanic thermal front, there is a strong positive correlation between sea surface temperature (SST) gradients and surface winds, and the marine atmospheric boundary layer is unstable over the warm side of the oceanic thermal front. The Gulf Stream is a notable example of an oceanic thermal front, and its warm side is often detected as enhanced backscatter in synthetic aperture radar (SAR) images. However, in some “anomalous” SAR images, low backscatter is sometimes observed on the warm side of the front, which seems inconsistent. Therefore, we propose a mechanism to interpret the generation of the low backscatter, based on interactions between ocean surface wind waves and intense nonlinear Ekman divergence. This mechanism is verified by showing that patterns in an observed anomalous SAR image are in good agreement with those in the simulated radar signature. In addition, this methodology and analysis demonstrate that SAR is potentially important for detecting and diagnosing small scale air‐sea interactions and upper ocean dynamics with strong vertical transports induced by submesoscale processes.

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Section: Physical Mechanism For Sar Low Backscattersmentioning

confidence: 99%

Section: Physical Mechanism For Sar Low Backscattersmentioning

confidence: 99%

Radar imaging of intense nonlinear Ekman divergence

Liu

Perrie

Kudryavtsev

et al. 2016

Geophysical Research Letters

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“…Hwang et al . [], Hwang and Fois [], and Elfouhaily et al . [] designed models of wave spectra that depend on wind speed and wave age.…”

Section: Response Time Analysismentioning

confidence: 99%

Response time of mean square slope to wind forcing: An empirical investigation

2016

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We present an empirical study of the response time of surface wave mean square slope to local wind forcing using data collected over 11 years by 46 discus buoys moored at a wide variety of locations. The response time is defined as the time lag at which the time dependence of the waves exhibits the highest correlation with that of the local wind speed. The response time at each location is found to be fairly stable, with the time varying between 0.4 and 1.8 h depending on the location. Examination of long‐term statistics reveals response time dependencies on wind speed magnitude, fetch, atmospheric stability, and wavelength. With the increasing reliance on satellite microwave remote sensing as a source of wind data, these results provide useful insights and bounds for their use.

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“…The comparison of wind retrieval using VV-polarization C-band SAR backscattering in hurricanes was reported in [41]. The results show RMSE of wind speed is 6.2-6.5 m/s against measurements from Stepped Frequency Microwave Radiometer (SFMR), due to winds encounter saturation problem as winds growing under tropical cyclone condition [30][31]. Therefore, SAR-derived wind speeds have a large deviation with reality in tropical cyclones.…”

Section: Application In Trpocial Cyclonementioning

confidence: 99%

“…This is because XMOD is tuned and validated through VV-polarization TS-X/TD-X images and the DWD with the wind speeds up to 25 m/s. Moreover, the signal saturation problem also exists for SAR in tropical cyclone [30,31].…”

Section: Introductionmentioning

confidence: 99%

An Empirical Algorithm for Wave Retrieval from Co-Polarization X-Band SAR Imagery

Shao

Wang

et al. 2017

Remote Sensing

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Abstract:In this study, we proposed an empirical algorithm for significant wave height (SWH) retrieval from TerraSAR-X/TanDEM (TS-X/TD-X) X-band synthetic aperture radar (SAR) co-polarization (vertical-vertical (VV) and horizontal-horizontal (HH)) images. As the existing empirical algorithm at X-band, i.e., XWAVE, is applied for wave retrieval from HH-polarization TS-X/TD-X image, polarization ratio (PR) has to be used for inverting wind speed, which is treated as an input in XWAVE. Wind speed encounters saturation in tropical cyclone. In our work, wind speed is replaced by normalized radar cross section (NRCS) to avoiding using SAR-derived wind speed, which does not work in high winds, and the empirical algorithm can be conveniently implemented without converting NRCS in HH-polarization to NRCS in VV-polarization by using X-band PR. A total of 120 TS-X/TD-X images, 60 in VV-polarization and 60 in HH-polarization, with homogenous wave patterns, and the coincide significant wave height data from European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis field at a 0.125 • grid were collected as a dataset for tuning the algorithm. The range of SWH is from 0 to 7 m. We then applied the algorithm to 24 VV and 21 HH additional SAR images to extract SWH at locations of 30 National Oceanic and Atmospheric Administration (NOAA) National Data Buoy Center (NDBC) buoys. It is found that the algorithm performs well with a SWH stander deviation (STD) of about 0.5 m for both VV and HH polarization TS-X/TD-X images. For large wave validation (SWH 6-7 m), we applied the empirical algorithm to a tropical cyclone Sandy TD-X image acquired in 2012, and obtained good result with a SWH STD of 0.3 m. We concluded that the proposed empirical algorithm works for wave retrieval from TS-X/TD-X image in co-polarization without external sea surface wind information.

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

Cited by 86 publications

References 46 publications

Radar imaging of intense nonlinear Ekman divergence

Radar imaging of intense nonlinear Ekman divergence

Response time of mean square slope to wind forcing: An empirical investigation

An Empirical Algorithm for Wave Retrieval from Co-Polarization X-Band SAR Imagery

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