Algorithms for sea surface wind parameter extraction from x-band shipborne nautical radar images

In this thesis, research for improving sea surface wind parameter extraction from shipborne X-band marine radar images is presented. First, the curve-fitting-based wind algorithms are investigated. To exclude the rain cases and low-backscatter images, a data quality control process is designed. Then, a dual-curve-fitting technique is proposed to enhance the wind retrieval performance under low sea states. This modified curve-fitting-based wind algorithm is 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. Secondly, the intensity-level-selection- (ILS-) based wind algorithms are studied. An additional process is implemented 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. Moreover, a harmonic function that is least-squares fitted to the selected range distances vector as a function of antenna look direction is applied. This modified ILS-based wind algorithm is applied to the same radar data. Compared with the original ILS-based algorithm, the modified one reduces the standard deviation (STD) of wind direction and speed by about 4◦ and 0.2 m/s, respectively. Also, the above mentioned two modified methods (dual-curve-fittingbased and modified ILS-based) are compared. Finally, X-band radar signatures of rain cells are elaborately described both in time and frequency domains. It is seen that the rain-contaminated image pixels are more uniformly bright than the wave echoes and radar retrieved wind results are thus overestimated. This property of “uniformly bright” is used to identify the portions that are more affected by rain. To mitigate the effects of rain on wind retrieval from X-band radar images, a novel texture-analysis-based data filtering process is presented and tested. By removing the data in the directions more affected by rain, significant improvements can be seen from the radar-derived wind results using both of the two wind algorithms