Global wind speed retrieval from sar

Horstmann, Jochen; Schiller, Helmut; Schulz‐Stellenfleth, Johannes; Lehner, Susanne

doi:10.1109/tgrs.2003.814658

Cited by 126 publications

(79 citation statements)

References 21 publications

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“…Therefore, the satellite SAR used in wind resource estimation has a brief history, having started at the turn of the millennium [7]. Satellite SAR wind maps have been compared to meteorological in situ measurement or meso-scale model results [8,9] in Europe; it is indicated that SAR wind retrievals was consistent with in situ measurement in areas of open ocean [10,11]. However, there is a lack of research into the working applicability of SAR data for offshore wind resource assessment in the SCS region.…”

Section: Introductionmentioning

confidence: 99%

Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea

et al. 2015

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Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean satellite winds and winds from WRF simulations in order to acquire the accurate reconstructed offshore winds which can be used for offshore wind resource assessment. First, wind speeds retrieved from Synthetic Aperture Radar (SAR) and Scatterometer ASCAT images were validated against in situ measurements from seven coastal meteorological stations in South China Sea (SCS). The wind roses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) and ASCAT agree well with these observations from the corresponding in situ measurements. The statistical results comparing in situ wind speed and SAR-based (ASCAT-based) wind speed for the whole co-located samples show a standard deviation (SD) of 2.09 m/s (1.83 m/s) and correlation coefficient of R 0.75 (0.80). When the offshore winds (i.e., winds directed from land to sea) are excluded, the comparison results for wind speeds show an improvement of SD and R, indicating that the satellite data are more credible over the open ocean. Meanwhile, the validation of satellite winds against the same co-located mast observations shows a OPEN ACCESSRemote Sens. 2015, 7 468 satisfactory level of accuracy which was similar for SAR and ASCAT winds. These satellite winds are then assimilated into the Weather Research and Forecasting (WRF) Model by WRF Data Assimilation (WRFDA) system. Finally, the wind resource statistics at 100 m height based on the reconstructed winds have been achieved over the study area, which fully combines the offshore wind information from multiple satellite data and numerical model. The findings presented here may be useful in future wind resource assessment based on satellite data.

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Section: Introductionmentioning

confidence: 99%

Offshore Wind Resources Assessment from Multiple Satellite Data and WRF Modeling over South China Sea

et al. 2015

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“…moderate winds (5 to 20 m/s) using the CMOD4 and CMOD_IFR2 have errors of ±2 m/s [23,34,35]. CMOD5 is applicable for higher wind speeds (>20 m/s), extending the dynamical range for C-band scatterometer data from 24 to 35 m/s.…”

Section: Wind Speed Retrievalmentioning

confidence: 99%

“…Well-calibrated SAR images can be used to routinely provide high spatial resolution ocean surface wind fields over a specified domain [19][20][21]. The retrieved wind speeds from SAR images have been refined and improved with an accuracy up to approximately 2 m/s in the wind speed range of 0-35 m/s [22][23][24]. SAR images have high spatial resolution, but irregular temporal and spatial coverage for a specified domain.…”

Section: Introductionmentioning

confidence: 99%

Reconstructed Wind Fields from Multi-Satellite Observations

Tang

Liu

et al. 2014

Remote Sensing

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Abstract:We present and validate a method of reconstructing high-resolution sea surface wind fields from multi-sensor satellite data over the Grand Banks of Newfoundland off Atlantic Canada. Six-hourly ocean wind fields from blended products (including multi-satellite measurements) with 0.25° spatial resolution and 226 RADARSAT-2 synthetic aperture radar (SAR) wind fields with 1-km spatial resolution have been used to reconstruct new six-hourly wind fields with a resolution of 10 km for the period from August 2008 to December 2010, except July 2009 to November 2009. The reconstruction process is based on the heapsort bucket method with topdown search and the modified Gauss-Markov theorem. The result shows that the mean difference between the reconstructed wind speed and buoy-estimated wind speed is smaller than 0.6 m/s, and the standard deviation is smaller than 2.5 m/s. The mean difference in wind direction between reconstructed and buoy estimates is 3.7°; the standard deviation is 40.2°. There is fair agreement between the reconstructed wind vectors and buoy-estimated ones.

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“…These coefficients were determined empirically in the case of the model functions CMOD4 by evaluation of scatterometer data of the European satellite ERS-1 and wind fields from the ECMWF (Stoffelen & Andersen 1997). CMOD4 has been applied successfully in wind speed retrieval from C-band VV polarised ERS SAR images (Lehner et al 1998;Horstmann et al 2002Horstmann et al , 2003 in which the accuracy of the algorithm was shown to be about 1 ms −1 in wind speed and about 22…”

Section: Sar Wind Measurementsmentioning

confidence: 99%

“…The direction of these streaks can either be retrieved using spectral methods (Lehner et al 1998;Horstmann et al 2000), or in the spatial domain by a method based on local gradients (Horstmann et al 2002;Koch 2003). The wind speed is derived from the normalised radar cross section (NRCS) measured by the SAR using semiempirical C-band models that were originally derived for scatterometers at an accuracy of about 20 degrees and 1 ms −1 (Lehner et al 1998;Horstmann et al 2000Horstmann et al , 2003.…”

Section: Introductionmentioning

confidence: 99%