Development and validation of altimeter wind speed algorithms using an extended collocated Buoy/Topex dataset

Gommenginger, Christine; Srokosz, Meric; Challenor, Peter; Cotton, P. D.

doi:10.1109/36.992782

Cited by 42 publications

(28 citation statements)

References 21 publications

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“…Hwang et al [7] conducted a statistical comparison of wind speed, wave height, and wave period derived from satellite altimeters and ocean buoys in the Gulf of Mexico region and concluded that the wind speed accuracy is significantly improved when local incidence angle modification by surface tilting is taken into account. Gommenginger et al [8] used a collocated ocean TOPography EXperiment (TOPEX) backscatter/buoy wind data set to show that altimeter wind speeds are affected by the degree of wave development and that the results can be underestimated by as much as 1.5 m/s in developing wind sea conditions. The addition of altimeter H s information causes a small but significant reduction of about 10% in wind speed root-mean-square error (rmse).…”

Section: Introductionmentioning

confidence: 99%

Relationships Between Ku-Band Radar Backscatter and Integrated Wind and Wave Parameters at Low Incidence Angles

Chu

Karaev

2012

IEEE Trans. Geosci. Remote Sensing

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By using ten years of collocated Precipitation Radar and buoy data, the relationships between Ku-band normalized radar cross section (σ • ) and integrated wind and wave parameters (e.g., significant wave height, wave period, wave steepness, and wave age) at low incidence angles are analyzed for different sea states using correlation and dependence analysis. The results show that the relationships are significantly different for different sea states. Second, the potential for inverting these parameters directly from a single σ • is investigated. The results reveal that the retrieval of wind speed above 10 m (U 10 ) is most suitable for a nonpure-swell sea and that the performance of significant wave height (H s ) retrieval decreases with the ratio of swell to wind waves. For wave period and wavelength, a feasible inversion can only be performed for a wind-wave-dominated sea. Wave steepness (δ a ) is strongly correlated with σ • in all sea states and can even be higher than that for U 10 in pure-swell seas. It is suggested from the present data set that real wave age (β) may be retrieved from a swell-dominated sea and, therefore, has a low-value cutoff. Furthermore, the extent to which U 10 retrieval depends on sea state is examined by calculating the correlation coefficient between σ • residuals and various standard wave parameters and, then, by determining the partial correlation coefficient between σ • and each wave parameter controlled by U 10 . Both results indicate that wind direction is better taken into account above 13.5 • . Moreover, an auxiliary parameter related to wave slope, such as wave steepness, would help to improve retrieval performance. Significant wave height data are of secondary use, but an implementation with real wave age information might not significantly improve wind speed retrieval. To improve the performance of H s and wave period retrieval, auxiliary information, such as wind speed, wave steepness, or wave age, is needed. Finally, multivariable empirical models are proposed.Index Terms-Integral wave parameter, low-incidence-angle remote sensing, radar cross section, radar signal analysis.

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Section: Introductionmentioning

confidence: 99%

Relationships Between Ku-Band Radar Backscatter and Integrated Wind and Wave Parameters at Low Incidence Angles

Chu

Karaev

2012

IEEE Trans. Geosci. Remote Sensing

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“…They typically operate in the 2-5 cm range of wavelengths, which makes the radar backscatter very sensitive to small-scale capillary waves generated by the wind, but also makes more challenging the retrieval in heavy precipitation conditions, such as those experienced during TCs. Radar altimeters are also used for wind speed retrieval, although they have much more limited spatial coverage than wide-swath imaging scatterometers [8]- [10]. SAR can provide wind measurements at high spatial resolution, and, for those operating at suitable wavelengths, even in the presence of rain [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

“…Global coverage with adequate temporal resolution can only be achieved through the use of satellites and, in particular, through sensors operating at microwave frequencies, that can measure the surface wind vector day and night, and can penetrate through clouds and rain (provided the wavelength is long enough). Scatterometers [1]- [7], Radar Altimeters [8]- [10] and Synthetic Aperture Radar (SAR) [11]- [13] are all active radar sensors with the capability of measuring sea surface wind speed. Scatterometers in particular, like QuikScat, ASCAT and OSCAT, are specifically designed to measure near-surface winds over the ocean by measuring the backscattered radar cross section and relating it to the wind speed -usually through an empirical Geophysical Model Function (GMF) [14]- [17].…”

Section: Introductionmentioning

confidence: 99%

Spaceborne GNSS-R Minimum Variance Wind Speed Estimator

Clarizia

Ruf

Jales

et al. 2014

IEEE Trans. Geosci. Remote Sensing

199

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A Minimum Variance (MV) wind speed estimator for Global Navigation Satellite System-Reflectometry (GNSS-R) is presented. The MV estimator is a composite of wind estimates obtained from five different observables derived from GNSS-R Delay-Doppler Maps (DDMs). Regression-based wind retrievals are developed for each individual observable using empirical geophysical model functions that are derived from NDBC buoy wind matchups with collocated overpass measurements made by the GNSS-R sensor on the United Kingdom-Disaster Monitoring Constellation (UK-DMC) satellite. The MV estimator exploits the partial decorrelation that is present between residual errors in the five individual wind retrievals. In particular, the RMS error in the MV estimator, at 1.65 m/s, is lower than that of each of the individual retrievals. Although they are derived from the same DDM, the partial decorrelation between their retrieval errors demonstrates that there is some unique information contained in them. The MV estimator is applied here to UK-DMC data, but it can be easily adapted to retrieve wind speed for forthcoming GNSS-R missions, including the UK's TechDemoSat-1 (TDS-1) and NASA's Cyclone Global Navigation Satellite System (CYGNSS).Index Terms-Delay-Doppler map, global navigation satellite systems (GNSS)-reflectometry, minimum variance (MV) estimator, ocean surface wind speed.

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“…In situ measurements using ships, buoys, shore-based stations and other sources are the most accurate ways to obtain sea wind information, but they are associated with drawbacks, such as high cost and coarse spatial coverage, considering the large scale of the ocean. At present, microwave scatterometers, e.g., ASCAT-A/B [1,2], HY-2 [3], Oceansat-2 [4], the full-polarimetric radiometer on WindSat [5], radar altimeters [6,7] and Synthetic Aperture Radar (SAR), are the primary remote sensing sensors that can be used to obtain all-weather and global coverage sea wind information. Each of the abovementioned types of wind sensors has advantages, as well as drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Derivation of Sea Surface Wind Directions from TerraSAR-X Data Using the Local Gradient Method

Wang

2016

Remote Sensing

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Derivation of sea surface wind direction is a key step of sea surface wind retrieval from spaceborne Synthetic Aperture Radar (SAR) data. This technical note describes an implementation of the Local Gradient (LG) method to derive sea surface wind directions at a scale of a few kilometers from X-band spaceborne SAR TerraSAR-X (TS-X) data in wide swath mode. The 180˝ambiguity in the derived sea surface wind direction is automatically eliminated using a single reference wind direction from external data sources. Several typical cases acquired in the North Sea were presented to demonstrate the derivation of sea surface wind direction under different wind situations using this method. The derived sea surface wind direction were further compared to atmospheric model prediction results. In addition, a practical method is introduced to address ambiguity in the derived sea surface wind directions using the LG method in a typhoon case with rotating surface wind structure. By interpolating the derived wind directions at a scale of kilometers, sea surface wind speeds with a spatial resolution of 500 m are subsequently retrieved using the X-band SAR sea surface wind Geophysical Model Function (GMF). The approach accomplished by combining the LG method with the X-band GMF for deriving sea surface wind in high spatial resolution demonstrates its potential for operational service.

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Development and validation of altimeter wind speed algorithms using an extended collocated Buoy/Topex dataset

Cited by 42 publications

References 21 publications

Relationships Between Ku-Band Radar Backscatter and Integrated Wind and Wave Parameters at Low Incidence Angles

Relationships Between Ku-Band Radar Backscatter and Integrated Wind and Wave Parameters at Low Incidence Angles

Spaceborne GNSS-R Minimum Variance Wind Speed Estimator

Derivation of Sea Surface Wind Directions from TerraSAR-X Data Using the Local Gradient Method

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