Remote sensing of the surface wind field over the coastal ocean via direct calibration of HF radar backscatter power
Kirincich, Anthony R.
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KeywordAtm/Ocean Structure/ Phenomena; Wind; Observational techniques and algorithms; Algorithms; In situ oceanic observations; Radars/Radar observations; Remote sensing; Surface observations
The calibration and validation of a novel approach to remotely sense surface winds using land-based high-frequency (HF) radar systems are described. Potentially available on time scales of tens of minutes and spatial scales of 2–3 km for wide swaths of the coastal ocean, HF radar–based surface wind observations would greatly aid coastal ocean planners, researchers, and operational stakeholders by providing detailed real-time estimates and climatologies of coastal winds, as well as enabling higher-quality short-term forecasts of the spatially dependent wind field. Such observations are particularly critical for the developing offshore wind energy community. An autonomous surface vehicle was deployed within the Massachusetts Wind Energy Area, located south of Martha’s Vineyard, Massachusetts, for one month, collecting wind observations that were used to test models of wind-wave spreading and HF radar energy loss, thereby empirically relating radar-measured power to surface winds. HF radar–based extractions of the remote wind speed had accuracies of 1.4 m s−1 for winds less than 7 m s−1, within the optimal range of the radar frequency used. Accuracies degraded at higher winds due to low signal-to-noise ratios in the returned power and poor resolution of the model. Pairing radar systems with a range of transmit frequencies with adjustments of the extraction model for additional power and environmental factors would resolve many of the errors observed.
Author Posting. © American Meteorological Society, 2016. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Atmospheric and Oceanic Technology 33 (2016): 1377-1392, doi:10.1175/JTECH-D-15-0242.1.
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