Wind speed and direction mapping with HF radar

HF radar systems are used in many parts of the world as a part of operational coastal ocean observing systems. Their primary product is surface current mapping from the coast to a range determined by radio frequency and environmental conditions. Initiatives to promote their use for wave measurement are now being developed. Obtaining reliable wind measurements has proved more difficult primarily because there is no direct physical relationship between the radar signal and the wave field. In this paper, a machine learning approach, previously demonstrated for radar data at the location of an in situ measurement, has been extended to allow for wind mapping using wind model data for training. Using data from three different radar deployments operating at different frequencies, a single machine learning model has been developed that can be applied to all three locations. A subset of the model data is used in the training and testing of the method, and accuracy is assessed using a mix of these data and data at all model positions within the radar field of view. The results show that the new wind speed measurements are significantly more accurate than those previously available using an inverse wind-wave model. Radar wind maps are consistent with, although show more spatial variability than, model or satellite winds. More validation with offshore wind masts is recommended.