A method of retrieving significant wave height based on shadowing from X-band marine radar images

ABSTRACT

In this paper, the shadow statistical method, which has the merit of without calibration, is further investigated for retrieving significant wave height (SWH) based on shadowing from X-band marine radar images. For the shadow statistical method, the Smith fitting function (SFF) is utilized to fit the illumination probability obtained from the radar image and calculate the SWH. By deeply investigating the estimation of wave steepness from the shadow image, it is found that the analytic solution of wave steepness is not obtained, due to the complementary error function and exponential term in the SFF. Meanwhile, several screening processes of gradually discarding illumination probability are required to obtain accurate root mean square (RMS) wave steepness, which makes the calculation time-consuming. To solve these problems, both the complementary error function and exponential term in the SFF are approximated under the working conditions of marine radar in practice, and the analytic solution of the wave steepness is described with the illumination probability. The shore-based X-band radar images at Pingtan station from November 9 to 17, 2014 are collected to validate the validity of the proposed method. By using 254 sets of radar data with the SWH range of 0.5 ~ 3.5 m, the experimental results demonstrate that the correlation coefficient (CC) of the estimated SWH between the proposed method and the traditional method is 0.996, and the root mean square error (RMSE) is 0.034. The average running time is significantly reduced from 2.76 s to 0.44 s.

KEYWORDS:

• Radar image
• Smith fitting function (SFF)
• illumination probability
• wave steepness
• Significant wave height (SWH)

Acknowledgements

The authors greatly appreciate the editors and anonymous reviewers for their efforts and time which they have spent on this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the Natural Science Foundation of Henan Province under [Grant No. 232300420447] and in part by the Key Scientific Research Project in Colleges and Universities of Henan Province under [Grant No. 22A170015].