Can synthetic aperture radars be used to estimate hurricane force winds?

Horstmann, Jochen; Thompson, D. R.; Monaldo, F.; Iris, Steve; Graber, Hans C.

doi:10.1029/2005gl023992

Cited by 73 publications

(33 citation statements)

References 13 publications

(22 reference statements)

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“…SAR-observed NRCS σ 0 is directly related to the sea surface roughness of short surface waves, and is widely used for ocean wind retrievals i.e., [32,44,45]. Thus, NRCS can represent information on wind sea (short wave roughness) and co-polarized (VV) NRCS has been introduced into SAR empirical SWH algorithms [19,20,23].…”

Section: Normalized Radar Cross Section (Nrcs)mentioning

confidence: 99%

Empirical Algorithm for Significant Wave Height Retrieval from Wave Mode Data Provided by the Chinese Satellite Gaofen-3

Wang¹,

Wang

Yang

et al. 2018

Remote Sensing

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Gaofen-3 (GF-3), the first Chinese civil C-band synthetic aperture radar (SAR), was successfully launched by the China Academy of Space Technology on 10 August 2016. Among its 12 imaging modes, wave mode is designed to monitor the ocean surface waves over the open ocean. An empirical retrieval algorithm of significant wave height (SWH), termed Quad-Polarized C-band WAVE algorithm for GF-3 wave mode (QPCWAVE_GF3), is developed for quad-polarized SAR measurements from GF-3 in wave mode. QPCWAVE_GF3 model is built using six SAR image and spectrum related parameters. Based on a total of 2576 WaveWatch III (WW3) and GF-3 wave mode match-ups, 12 empirical coefficients of the model are determined for 6 incidence angle modes. The validation of the QPCWAVE_GF3 model is performed through comparisons against independent WW3 modelling hindcasts, and observations from altimeters and buoys from January to October in 2017. The assessment shows a good agreement with root mean square error from 0.5 m to 0.6 m, and scatter index around 20%. In particular, applications of the QPCWAVE_GF3 model in SWH estimation for two storm cases from GF-3 data in wave mode and Quad-Polarization Strip I mode are presented respectively. Results indicate that the proposed algorithm is suitable for SWH estimation from GF-3 wave mode and is promising for other similar data.

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Section: Normalized Radar Cross Section (Nrcs)mentioning

confidence: 99%

Empirical Algorithm for Significant Wave Height Retrieval from Wave Mode Data Provided by the Chinese Satellite Gaofen-3

Wang¹,

Wang

Yang

et al. 2018

Remote Sensing

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“…The definition of other parameters in Equations (1) to (8) can be found in Figure 1. The simulated brightness temperature data of group A in Figure 1a are the results under calm ocean conditions.…”

Section: Methodsmentioning

confidence: 99%

“…Before estimating the model coefficients, we need to calculate the parameter 6(10)P − (P = V or H) with Equations (4) and (8). First, it need to estimate the calm ocean microwave emissions at both 6 and 10 GHz using Equations (10) and (11)…”

Section: Comparison Of the Klein-swift Model And The Ellison Modelmentioning

confidence: 99%

Estimate of Hurricane Wind Speed from AMSR-E Low-Frequency Channel Brightness Temperature Data

Zhang

Yin²,

Shi

et al. 2018

Atmosphere

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Abstract:Two new parameters (W6H and W6V) were defined that represent brightness temperature increments for different low-frequency channels due to ocean wind. We developed a new wind speed retrieval model inside hurricanes based on W6H and W6V using brightness temperature data from AMSR-E. The AMSR-E observations of 12 category 3-5 hurricanes from 2003 to 2011 and corresponding data from the H*wind analysis system were used to develop and validate the AMSR-E wind speed retrieval model. The results show that the mean bias and the overall root-mean-square (RMS) difference of the AMSR-E retrieved wind speeds with respect to H*wind (HRD Real-time Hurricane Wind Analysis System) analysis data were −0.01 m/s and 2.66 m/s, respectively. One case study showed that W6H and W6V were less sensitive to rain than the observed AMSR-E C-band and X-band brightness temperature data. The AMSR-E retrieval model was further validated by comparing the retrieved wind speeds against stepped-frequency microwave radiometer (SFMR) measurements. The comparison showed an RMS difference of 3.41 m/s and a mean bias of 0.49 m/s.

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“…Du and Vachon (2003) developed a wavelet technology to extract hurricane eye information from eight Radarsat-1 SAR images. Limited case studies by Horstmann et al (2005) and Shen et al (2006) showed the capabilities of using single-polarization Radarsat-1 SAR for high-resolution wind speed mapping with existing geophysical model functions (GMF). Yang et al (2011) later showed that the wind retrieval accuracy is, however, highly sensitive to the NRCS calibration accuracy.…”

Section: The Abstract For This Article Can Be Found In This Issuementioning

confidence: 99%