Cross‐Polarization GMF for High Wind Speed and Surface Stress Retrieval

Troitskaya, Yuliya; Abramov, V. I.; Baidakov, G. A.; Ermakova, Olga; Zuikova, E.M.; Sergeev, D. A.; Ermoshkin, Alexey V.; Kazakov, V. I.; Kandaurov, Alexander; Rusakov, Nikita; Poplavsky, Evgeny; Vdovin, Maxim

doi:10.1029/2018jc014090

Cited by 13 publications

(6 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RADARSAT-1 was one of the early operational SAR systems, and some of the images have problematic distortions (Vachon et al, 1999), i.e. when stitching the subswaths together or there are issues with the geolocation.…”

Section: Methodsmentioning

confidence: 99%

“…Two findings speak for the generality of this approach: (i) intercalibration tables derived over northern Europe can be applied for the US East Coast; (ii) applying the suggested intercalibration method to RADARSAT-1 data reduces the bias to 0.03 m s −1 . The intercalibration should in no way substitute efforts to better understand scattering mechanisms in order to improve the calibration of NRCS as well as GMFs for wind retrieval (Troitskaya et al, 2018). Tuning NRCS values is an application-driven approach, which is necessary at present in order to produce wind maps with consistently low biases.…”

Section: Validation Of Intercalibrated Sar Wind Archive With Buoysmentioning

confidence: 99%

See 1 more Smart Citation

US East Coast synthetic aperture radar wind atlas for offshore wind energy

Ahsbahs¹,

Maclaurin

Draxl

et al. 2020

Wind Energ. Sci.

View full text Add to dashboard Cite

Abstract. We present the first synthetic aperture radar (SAR) offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from RADARSAT-1, Envisat, and Sentinel-1A/B are processed to wind maps using the geophysical model function (GMF) CMOD5.N. Extensive comparisons with 6008 collocated buoy observations of the wind speed reveal that biases of the individual systems range from −0.8 to 0.6 m s−1. Unbiased wind retrievals are crucial for producing an accurate wind atlas, and intercalibration of the SAR observations is therefore applied. Wind retrievals from the intercalibrated SAR observations show biases in the range of to −0.2 to 0.0 m s−1, while at the same time improving the root-mean-squared error from 1.67 to 1.46 m s−1. The intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas at the height 10 m a.s.l. (above sea level). The SAR wind atlas is used as a reference to study wind resources derived from the Wind Integration National Dataset Toolkit (WTK), which is based on 7 years of modelling output from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation in wind resources and show that model outputs lead to lower coastal wind speed gradients than those derived from SAR. Areas designated for offshore wind development by the Bureau of Ocean Energy Management are investigated in more detail; the wind resources in terms of the mean wind speed show spatial variations within each designated area between 0.3 and 0.5 m s−1 for SAR and less than 0.2 m s−1 for the WTK. Our findings indicate that wind speed gradients and variations might be underestimated in mesoscale model outputs along the US East Coast.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Validation Of Intercalibrated Sar Wind Archive With Buoysmentioning

confidence: 99%

US East Coast synthetic aperture radar wind atlas for offshore wind energy

Ahsbahs¹,

Maclaurin

Draxl

et al. 2020

Wind Energ. Sci.

View full text Add to dashboard Cite

show abstract

“…For comparison, we used an array of data obtained on cross polarization in a wide range of wind speeds by (Hwang et al, 2015). In (Troitskaya et al, 2018) the authors used data from (Hwang et al, 2015) to retrieve the dependence of the NRCS on * u . Obviously, Fig.…”

Section: Sentinel-1 Satellite Remote Sensing Data Analysismentioning

confidence: 99%

“…The first works on the use of active microwave methods for wind tangential turbulent stress, or wind friction velocity retrieval presented recently (see, for example, Liu, Xie, 2014, Liu, Tang, 2016, Troitskaya et al, 2018, Ermakova et al, 2019. This approach seems preferable, since the small-scale roughness prescribing the magnitude of the backscattered signal is mainly related to the turbulent wind stress (Liu, Xie, 2014), and the dependence of the NRCS on the turbulent stress turns out to be stronger than on the wind speed (see, Jones, Schroeder 1978, Weissman et al, 1994.…”

mentioning

confidence: 99%

Toward the GMF for Wind Speed and Surface Stress Retrieval in Hurricanes Based on the Collocated GPS-Dropsonde and Remote Sensing Data

Ermakova

Sergeev

Rusakov

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Self Cite

View full text Add to dashboard Cite

This paper describes the first step towards the development of the geophysical model function (GMF) for the retrieval of wind speed and wind stress in hurricanes, based on developing a relation between the cross-polarized satellite SAR data from Sentinel-1 and winds/stress observed from collocated NOAA GPS-dropsondes data. Field measurements and remote sensing data for tropical cyclones in the Atlantic Ocean were analyzed. Using the data measured by GPS-dropsondes, average wind velocity profiles were obtained, while the parameters of the wind boundary layer (drag coefficient and friction velocity) were restored from the "wake" part of the velocity profiles using the self-similarity property. The self-similarity of the velocity profile "defect" in the boundary layer, known from the fluid dynamics, was used to retrieve the parameters of the atmospheric boundary layer (the surface wind velocity, drag coefficient and friction velocity) from the dropsonde wind velocity profiles in 10 major hurricanes. Based on the processing of the measurements in the hurricanes Irma 2017/09/07, Maria 2017/09/21 and 2017/09/23, at a time close to the time of acquisition of the Sentinel-1 images, the dependencies of the cross-polarized normalized radar crosssection (NRCS) on the wind speed and wind friction velocity were obtained and used for constructing the GMFs.

show abstract

“…Two findings speak to the generality of this approach: i) Intercalibration tables derived over northern Europe can be applied for the US East Coast; ii) applying the suggested intercalibration method to Radarsat-1 data reduces the bias to 0.03 m/s. This concept should in no way substitute efforts to better understand scattering mechanisms that improve GMFs (Troitskaya et al, 2018). Tuning NRCS values is an application-driven approach to produce wind maps with consistently low bias.…”

Section: Discussionmentioning

confidence: 99%

US East Coast synthetic aperture radar wind atlas for offshore wind energy

Ahsbahs¹,

Maclaurin²,

Draxl³

et al. 2019

Preprint

View full text Add to dashboard Cite

Abstract. We present the first synthetic aperture radar (SAR)-based offshore wind atlas of the US East Coast from Georgia to the Canadian border. Images from Radarsat-1, Envisat, Sentinel-1A, and Sentinel-1B are processed to wind maps using the Geophysical Model Function (GMF) CMOD5.N. Extensive comparisons with 6,008 collocated buoy observations revealed that biases of the individual system range from −0.8 to 0.6 m/s. Unbiased wind retrievals are crucial for producing an accurate wind atlas and intercalibration for correcting these biases by adjusting the normalized radar cross section is applied. The intercalibrated SAR observations show biases in the range of to −0.2 to 0.0 m/s, while at the same time improving the root mean squared error from 1.67 to 1.46 m/s. These intercalibrated SAR observations are, for the first time, aggregated to create a wind atlas. Monthly averages are used to correct artefacts from seasonal biases. The SAR wind atlas is used as a reference to study wind resources derived from the Weather Research and Forecasting (WRF) model. Comparisons focus on the spatial variation of wind resources and show that model results estimate lower coastal wind speed gradients than those from SAR. At sites designated for offshore wind development by the Bureau of Ocean Energy Management, mean wind speeds typically vary between 0.3 and 0.5 m/s for SAR and less than 0.2 m/s for the WRF model within each site. Findings indicate that wind speed gradients and variation might be underestimated in mesoscale model outputs along US East Coast.

show abstract

Cross‐Polarization GMF for High Wind Speed and Surface Stress Retrieval

Cited by 13 publications

References 34 publications

US East Coast synthetic aperture radar wind atlas for offshore wind energy

US East Coast synthetic aperture radar wind atlas for offshore wind energy

Toward the GMF for Wind Speed and Surface Stress Retrieval in Hurricanes Based on the Collocated GPS-Dropsonde and Remote Sensing Data

US East Coast synthetic aperture radar wind atlas for offshore wind energy

Contact Info

Product

Resources

About