Xinlong Liu

Comparison of Wave Height Measurement Algorithms for Ship-Borne X-Band Nautical Radar

Abstract. In this article, a comparison of signal-to-noise ratio (SNR) and shadowing-based algorithms for ocean wave height estimation from ship-borne X-band nautical radar sea surface images is presented. Modifications, including selecting a subarea along the upwind direction and smoothing the edge pixel intensity histogram, are made to the original shadowing algorithm to achieve more accurate wave height measurements. Tests of the algorithms are conducted using radar and buoy data acquired in a sea trial in the North Atlantic Ocean off the east coast of Canada. Compared with the original shadowing algorithm, the modified algorithm improves the wave height estimation with a decrease of about 1.70 m in the root mean square (RMS) difference with respect to in situ measurements. In addition, the result of the comparison shows that the modified shadowing algorithm produces more accurate wave heights than the SNR method with improvements of about 0.05 in the correlation coefficient and about 0.04 m in the RMS difference.

Wave Height Estimation from Shipborne X-Band Nautical Radar Images

A shadowing-analysis-based algorithm is modified to estimate significant wave height from shipborne X-band nautical radar images. Shadowed areas are first extracted from the image through edge detection. Smith’s function fit is then applied to illumination ratios to derive the root mean square (RMS) surface slope. From the RMS surface slope and the mean wave period, the significant wave height is estimated. A data quality control process is implemented to exclude rain-contaminated and low-backscatter images. A smoothing scheme is applied to the gray scale intensity histogram of edge pixels to improve the accuracy of the shadow threshold determination. Rather than a single full shadow image, a time sequence of shadow image subareas surrounding the upwind direction is used to calculate the average RMS surface slope. It has been found that the wave height retrieved from the modified algorithm is underestimated under rain and storm conditions and overestimated for cases with low wind speed. The modified method produces promising results by comparing radar-derived wave heights with buoy data, and the RMS difference is found be 0.59 m.

Wind Direction Estimation From Rain-Contaminated Marine Radar Data Using the Ensemble Empirical Mode Decomposition Method

Two ensemble empirical mode decomposition (EEMD)-based methods are presented to retrieve wind direction from rain-contaminated X-band nautical radar sea surface images. Each radar image is first decomposed into disparate intrinsic mode function (IMF) components using 1-D EEMD or 2-D EEMD. Then, the standard deviation of one IMF component or the combination of several IMF components as a function of azimuth is least-squares fitted to a harmonic function to determine the wind direction. Tests of the proposed algorithms are conducted by employing radar and anemometer data collected in a sea trial during rain events off the east coast of Canada. The results show that compared with the 1-D discrete-Fourier-transform-based method, both the 1-D- and 2-D-EEMD-based algorithms improve the wind direction results in rain events, showing a reduction of 7.4° and 8.7°, respectively, in the root-mean-square difference with respect to the reference.

Ocean Wind and Wave Measurements Using X-Band Marine Radar: A Comprehensive Review

Ocean wind and wave parameters can be measured by in-situ sensors such as anemometers and buoys. Since the 1980s, X-band marine radar has evolved as one of the remote sensing instruments for such purposes since its sea surface images contain considerable wind and wave information. The maturity and accuracy of X-band marine radar wind and wave measurements have already enabled relevant commercial products to be used in real-world applications. The goal of this paper is to provide a comprehensive review of the state of the art algorithms for ocean wind and wave information extraction from X-band marine radar data. Wind measurements are mainly based on the dependence of radar image intensities on wind direction and speed. Wave parameters can be obtained from radar-derived wave spectra or radar image textures for non-coherent radar and from surface radial velocity for coherent radar. In this review, the principles of the methodologies are described, the performances are compared, and the pros and cons are discussed. Specifically, recent developments for wind and wave measurements are highlighted. These include the mitigation of rain effects on wind measurements and wave height estimation without external calibrations. Finally, remaining challenges and future trends are discussed.

Wind direction determination from rain-contaminated X-band radar images

A two-dimensional ensemble empirical mode decomposition (2D-EEMD)-based method is presented to improve wind direction retrieval from rain-contaminated X-band nautical radar sea surface images. 2D-EEMD is first implemented to decompose each rain-contaminated radar image into several intrinsic mode function (IMF) components. Then, a harmonic function that is least-squares fitted to the standard deviation of the first IMF component as a function of azimuth is used to retrieve the wind direction. Radar and anemometer data acquired in a sea trial off the east coast of Canada under rain conditions are employed to test the algorithm. The result shows that, compared to the traditional curve fitting method, the proposed method improves the wind direction results in rain events, showing a reduction of 35.9° in the root-mean-square (RMS) difference with respect to the reference.

An Empirical Mode Decomposition Method for Sea Surface Wind Measurements From X-Band Nautical Radar Data

In this paper, sea surface wind direction and speed are obtained from X-band nautical radar images. A data control strategy is proposed to distinguish rain-free and rain-contaminated radar data. The radar data are decomposed by an ensemble empirical mode decomposition method into several intrinsic mode functions (IMFs) and a residual. A normalization scheme is applied to the first IMF to obtain the amplitude modulation (AM) component. Wind direction is determined from the residual for the rain-free and high-wind-speed rain-contaminated data, and from the AM portion of the first IMF for the low-wind-speed rain-contaminated data, based on curve fitting a harmonic function. Wind speed is determined from a combination of the residual and the AM part of the first IMF for both rain-free and rain-contaminated data using a logarithmic relationship. Results employing ship-borne radar and anemometer data collected in a sea trial off the east coast of Canada are presented. The root-mean-square differences for wind direction and speed measurements are 11.5° and 1.31 m/s, respectively, compared with reference values from anemometers.

Estimation of Significant Wave Height From X-Band Marine Radar Images Based on Ensemble Empirical Mode Decomposition

In this letter, an ensemble empirical mode decomposition (EEMD)-based method is proposed to estimate significant wave height (SWH) from the X-band marine radar sea surface images. First, the data sequence in each radial direction of a radar subimage is decomposed by the EEMD into several intrinsic mode functions (IMFs). A normalization scheme is then applied to the IMFs to obtain their amplitude modulation components. Finally, by adopting a linear model, the SWH is estimated from the sum of the amplitudes from the second to the fifth modes. The method is tested using radar and buoy data collected in a sea trial off the east coast of Canada. The root-mean-square differences with respect to the buoy reference for the SWH estimations using the traditional signal-to-noise-based method, a recent shadowing-based method, and the proposed technique are 0.78, 0.48, and 0.36 m, respectively. The result indicates that the proposed technique produces improvement in the SWH measurements.

Wind speed determination from X-band nautical radar images

A new method is presented for estimation of wind speed from X-band nautical radar sea surface images. Ensemble empirical mode decomposition (EEMD) is first applied to the radar data. A normalization scheme is then used to obtain the amplitude modulation (AM) part of the first intrinsic mode function (IMF). Wind speed is determined from a combination of the first IMF and the residual using a logarithmic relationship. The method can be applied to both rain-free and rain-contaminated radar images. Radar and anemometer data collected in a sea trial off the east coast of Canada are employed for the test. Compared to the spectral-analysis-based method, the proposed method improves the wind speed result with an increase of about 0.06 in the correlation coefficient and a decrease of about 0.28 m/s in the root-mean-square (RMS) difference with respect to the reference.

Analysis of rain effects on wave height estimation from X-band nautical radar images

In this paper, the analysis of rain effects on wave height estimation from X-band nautical radar sea surface images is presented. A modified shadowing-analysis-based algorithm is applied to the radar data acquired in a sea trial in the North Atlantic Ocean under both rainy and rain-free conditions. The result shows that rain enhances the image pixel intensity and reduces shadowed areas, leading to the underestimation of wave heights. An increase of about 0.31 m in the bias with respect to in-situ measurements is found in the wave height retrieved from rain contaminated data compared to the results obtained from data without rain.