Cross-Polarized Synthetic Aperture Radar: A New Potential Measurement Technique for Hurricanes

Zhang, Biao; Perrie, William

doi:10.1175/bams-d-11-00001.1

Cited by 210 publications

(137 citation statements)

References 36 publications

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“…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]. Besides, C-band cross-polarization (VH or HV) has demonstrated good performance to extract high winds even in hurricanes when saturation was observed in co-polarization i.e., [46,47]. Thus, the SAR SWH empirical model could be expected to provide better performance on estimation of wind wave energy in high sea state by taking VH NRCS into consideration (see Section 5.1 for detailed discussion).…”

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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“…As one of the encouraging achievements of these cross-polarization attempts, the RS-2 SAR cross-polarization NRCS first exhibited a strong linear sensitivity to extreme wind speeds [13,14], when the NRCS saturation was observed in co-polarization. This unique sensitivity for cross-polarization data is mainly attributed to the contribution of breaking waves [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…This unique sensitivity for cross-polarization data is mainly attributed to the contribution of breaking waves [15,16]. Some cross-polarization GMFs have been developed based on this sensitivity [13][14][15][16][17][18][19][20]. These models are simple and considered more suitable for high wind conditions.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Analysis of Chinese GF-3 SAR Quad-Polarization Measurements to Extract Winds in Each Polarization

et al. 2017

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This study analyzed the noise equivalent sigma zero (NESZ) and ocean wind sensitivity for Chinese C-band Gaofen-3 (GF-3) quad-polarization synthetic aperture radar (SAR) measurements to facilitate further operational wind extraction from GF-3 data. Data from the GF-3 quad-polarization SAR and collocated winds from both NOAA/NCEP Global Forecast System (GFS) atmospheric model and National Data Buoy Center (NDBC) buoys were used in the analysis. For NESZ, the co-polarization was slightly higher compared to the cross-polarization. Regarding co-polarization and cross-polarization, NESZ was close to RadarSAT-2 and Sentinel-1 A. Wind sensitivity was analyzed by evaluating the dependence on winds in terms of normalized radar cross-sections (NRCS) and polarization combinations. The closest geophysical model function (GMF) and the polarization ratio (PR) model to GF-3 data were determined by comparing data and the model results. The dependence of co-polarized NRCS on wind speed and azimuth angle was consistent with the proposed GMF models. The combination of CMOD5 and CMOD5.N was considered to be the closest GMF in co-polarization. The cross-polarized NRCS exhibited a strong linear relationship with moderate wind speeds higher than 4 m·s −1 , but a weak correlation with the azimuth angle. The proposed model was considered as the closest GMF in cross-polarization. For polarization combinations, PR and polarization difference (PD) were considered. PR increased only with the incidence angle, whereas PD increased with wind speed and varied with azimuth angle. There were three very close PR models and each can be considered as the closest. Preliminary results indicate that GF-3 quad-polarization data are valid and have the ability to extract winds in each polarization.

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“…Cross-polarization is essentially independent of wind direction and local incidence angle but has a linear relationship with respect to wind speed [24]. A C-band cross-polarized ocean backscatter model (C-2PO) relating NRCS in VH polarization σ 0 V H to equivalent neutral wind speed U 10 at 10-m height reference height, has been developed using RADARSAT-2 fine quad-polarization mode SAR data and collocated buoy observations [25,26]. RADARSAT-2 fine quad-polarization mode SLC SAR data are characterized by a nominal spatial resolution of 8.0 × 5.4 m in azimuth and range.…”

Section: Retrieval Methods Of Winds From Radarsat-2 Sar Imagesmentioning

confidence: 99%

Ocean Wind and Current Retrievals Based on Satellite SAR Measurements in Conjunction with Buoy and HF Radar Data

et al. 2017

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A total of 168 fully polarimetric synthetic-aperture radar (SAR) images are selected together with the buoy measurements of ocean surface wind fields and high-frequency radar measurements of ocean surface currents. Our objective is to investigate the effect of the ocean currents on the retrieved SAR ocean surface wind fields. The results show that, compared to SAR wind fields that are retrieved without taking into account the ocean currents, the accuracy of the winds obtained when ocean currents are taken into account is increased by 0.2-0.3 m/s; the accuracy of the wind direction is improved by 3-4 • . Based on these results, a semi-empirical formula for the errors in the winds and the ocean currents is derived. Verification is achieved by analysis of 52 SAR images, buoy measurements of the corresponding ocean surface winds, and high-frequency radar measurements of ocean currents. Results of the comparisons between data obtained by the semi-empirical formula and data measured by the high-frequency radar show that the root-mean-square error in the ocean current speed is 12.32 cm/s and the error in the current direction is 6.32 • .

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Cross-Polarized Synthetic Aperture Radar: A New Potential Measurement Technique for Hurricanes

Abstract: A new means of retrieving wind measurements from satellite data eliminates need for external wind direction and radar incidence angle inputs and provides linear response at high enough wind speeds for observing hurricanes.

Cited by 210 publications

References 36 publications

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

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

Preliminary Analysis of Chinese GF-3 SAR Quad-Polarization Measurements to Extract Winds in Each Polarization

Ocean Wind and Current Retrievals Based on Satellite SAR Measurements in Conjunction with Buoy and HF Radar Data

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