Inverse estimation of wind from the waves measured by high-frequency radar

Abstract

In this work, an attempt was made to derive wind speeds from the wave parameters recorded by a high-frequency (HF) radar by resorting to the techniques of an artificial neural network (ANN) and a model tree (MT) and by considering it as an inverse-modelling problem. The time series of significant wave height, average wave period, wave direction and wind direction collected over the years 2007 and 2008 by the Bodega Marine Laboratory (BML) at the Bodega Bay, California, were used along with the corresponding wind speeds measured by a floating buoy in the vicinity. The ANN and MT models were trained and tested using alternative data splits to assess their performance over varying sample sizes. Both these methods worked very well in this application, with the ANN showing better flexibility in model fitting. This study thus indicates that data-driven methods can be effectively used to derive unobserved wind speed values in HF radar measurements.