Comparison of Algorithms for Wind Parameters Extraction From Shipborne X-Band Marine Radar Images
In this paper, curve-fitting and intensity-level-selection (ILS)-based algorithms for wind parameter extraction from shipborne X-band nautical radar images are investigated. First, to exclude the rain cases and low-backscatter images, a data quality control process is designed for both algorithms. An additional process is then introduced for the ILS-based method to improve the accuracy of wind measurements, including the recognition of blockages and islands in the temporally integrated radar images. For the low sea states, a dual-curve-fitting is proposed. These wind algorithms are tested using radar images and shipborne anemometer data collected on the east coast of Canada. It is shown that the dual-curve-fitting algorithm produces improvements in the mean differences between the radar and the anemometer results for wind direction and speed of about 5.7° and 0.3 m/s, respectively, under sea states with significant wave height lower than 2.30 m. Also, a harmonic function that is least-squares fitted to the selected range distances vector as a function of antenna look direction is applied. Compared with the original ILS-based algorithm, the modified procedure reduces the standard deviation for wind direction and speed by about 4° and 0.2 m/s, respectively. Finally, the performance of these two modified methods are compared.