Since rain alters the histogram pattern of radar images, rain-contaminated radar data can be identified. In this article, a support vector machine (SVM)-based method for rain detection using X-band marine radar images is presented. First, the normalized histogram bin values for each image are extracted and combined into feature vector. Then, SVMs are employed to classify between rain-free and rain-contaminated images. Radar images and simultaneous rain rate data collected from a sea trial in North Atlantic Ocean are utilized for model training and testing. Comparison with the zero pixel percentage (ZPP) threshold method shows that the SVM-based method obtains higher detection accuracy, with 98.4% for the Decca radar data and 99.7% for the Furuno radar. It is also found that as the total number of bins does not significantly affect detection accuracy, the proposed method can be applied to different radar systems directly with a suitable number of bins. In addition, compared to the ZPP threshold method, the SVM-based method proves to be more robust even with limited training samples.

A common source of interference in high-frequency (HF) surface wave (HFSW) radars is radio frequency interference (RFI). Its existence inhibits the detection performance of HFSW radars since its amplitude can mask the sea echoes. On the basis of the analysis of RFI characteristics, a new RFI mitigation algorithm based on inverse temporal windowing and complex empirical mode decomposition (CEMD) is proposed in this letter. In this method, echoes containing RFI are decomposed into a number of intrinsic mode functions (IMFs) via CEMD and then the inverse temporal windowing technique is applied to each IMFs in the time domain. Test results show that the proposed method outperforms the conventional method in simulated and practical conditions and can effectively mitigate RFI without losing sea echoes.

Wind sea and swell representing different weather conditions generally coexist in both open waters and coastal areas, which results in bimodal or multipeaked features in directional wave spectrum. Because they make wave parameters such as significant wave height and mean wave period of the mixed sea state less meaningful, the processes of separation and identification of wind sea and swell are crucial. Consistent wind sea and swell results can be obtained by a commonly used method based on wave age (WA) with the directional wave spectrum and wind velocity. However, the subjective dependence of wave age threshold selection and the required wind information restrict the application of this method. In this study, a practical method based on the overshoot phenomenon (OP) in wind-generated waves is proposed to extract wind sea and swell from the directional wave spectrum without any other meteorology information. Directional wave spectra derived from an S-band Doppler radar deployed on the coast of the South China Sea have been utilized as the datasets to investigate the performance of both methods. The proposed OP method is then validated by comparing it with the WA method and the verifying results are presented.

In this paper, a novel shore-based S-band microwave Doppler coherent wave radar (Microwave Ocean Remote SEnsor (MORSE)) is designed to improve wave measurements. Marine radars, which operate in the X band, have been widely used for ocean monitoring because of their low cost, small size and flexibility. However, because of the non-coherent measurements and strong absorption of X-band radio waves by rain, these radar systems suffer considerable performance loss in moist weather. Furthermore, frequent calibrations to modify the modulation transfer function are required. To overcome these shortcomings, MORSE, which operates in the S band, was developed by Wuhan University. Because of the coherent measurements of this sensor, it is able to measure the radial velocity of water particles via the Doppler effect. Then the relation between the velocity spectrum and wave height spectrum can be used to obtain the wave height spectra. Finally, wave parameters are estimated from the wave height spectra by the spectrum moment method. Comparisons between MORSE and Waverider MKIII are conducted in this study, and the results, including the non-directional wave height spectra, significant wave height and average wave period, are calculated and displayed. The correlation coefficient of the significant wave height is larger than 0.9, whereas that of the average wave period is approximately 0.4, demonstrating the effectiveness of MORSE for the continuous monitoring of ocean areas with high accuracy.

Shore-based coherent microwave radar and high-frequency (HF) surface wave radar are two components of a rapidly emerging set of technologies suitable for ocean wave remote sensing. To investigate their differences, this paper describes and analyzes the relationship between the water particle velocity and the wave height spectrum based on linear wave theory which underpins the algorithms developed for the analysis of data collected by coherent microwave radar. The backscatter mechanism which addresses the interaction of the HF radio waves with the ocean surface waves, as well as the empirical method adopted in our HF radar is also presented. The wave characteristics observed by the shore-based coherent S-band radar [Microwave Ocean Remote SEnsor (MORSE)] are analyzed. A multifrequency HF (MHF) radar based on a circular receiving array, which is capable of sensing waves up to 100-km offshore, is also introduced. An intercomparison of the wave height measurements obtained from the MORSE, MHF radar, and wave buoy is made. The comparison indicates that the wave heights measured by the MORSE and the MHF radar are consistent with the buoy-derived wave heights, with the root-mean-square differences (RMSDs) of 0.27 and 0.37 m, respectively.