Wind Direction Extraction Research Articles

Development of a Fast Convergence Gray-Level Co-Occurrence Matrix for Sea Surface Wind Direction Extraction from Marine Radar Images

The new sea surface wind direction from the X-band marine radar image is proposed in this study using a fast convergent gray-level co-occurrence matrix (FC-GLCM) algorithm. First, the radar image is sampled directly without the need for interpolation due to the algorithm’s application of the GLCM to the polar co-ordinate system, which reduces the inaccuracy caused by image transformation. An additional process is then to merge the fast convergence method with the optimized GLCM so that the circular transition between rough and fine estimates is acquired, resulting in the fast convergence and accuracy improvement of the GLCM. Furthermore, the algorithm will affect the GLCM spatial distribution while calculating it, and it can automatically resolve the 180° ambiguity problem of sea surface wind direction retrieved from radar images. Finally, the proposed method is applied to 1436 X-band marine radar sequences collected from the coast of the East China Sea. Compared with in situ anemometer data, the correlation coefficient is as high as 0.9268, and the RMSE is 4.9867°. The new method was also tested under diverse sea conditions. The FC-GLCM wind direction results against the adaptive reduced method (ARM), energy spectrum method (ESM), and the traditional GLCM (T-GLCM) method produced the best stability and accuracy, in which the RMSE decreased by 91.6%, 67.7%, and 18.1%, respectively.

Extraction of wind direction from the HF hybrid sky-surface wave radar sea echoes

In this article, we develop an experiment to obtain the sea state parameters by the HFHSSWR and the in situ instruments. The wind direction is extracted, and the results are compared with the in situ data. In addition, the wind direction ambiguity was eliminated by the ML method. The results show that it is feasible to extract the wind direction using the HFHSSWR.

Wind Direction Extraction from SAR in Coastal Areas

This paper aims to illustrate and test a method, based on the Two-Dimensional Continuous Wavelet Transform (2D-CWT), developed to extract the wind directions from the Synthetic Aperture Radar (SAR) images. The knowledge of the wind direction is essential to retrieve the wind speed by using the radar-backscatter versus wind speed algorithms. The method has been applied to 61 SAR images from different satellites (Envisat, COSMO-SkyMed, Radarsat-2 and Sentinel-1A,B), and the results have been compared with the analysis wind fields from the European Centre for Medium-range Weather Forecasts (ECMWF) model, with in situ reports and with scatterometer data when available. The 2D-CWT method provides satisfactory results, both in areas a few kilometres from the coast and offshore. It is reliable as it produces good direction estimates, no matter what the characteristics of the SAR are. Statistics reports a success in the SAR wind direction estimates in 95% of cases (in 83% of cases the SAR-ECMWF wind direction difference is < ± 20 ∘ , in 92 % < ± 30 ∘ ) with a mean directional bias B θ < 7 ∘ . The SAR derived wind directions cannot be said to be validated, as the data available at present cannot be really representative of the wind field in the coastal area. However, the figures given by SAR winds are highly valuable even not properly validated, providing an independent and unique view of the spatial variability of the wind over the sea, which is possible by using the 2D-CWT method to derive the wind directions.

Second-Order Ocean Surface Cross Section for Shipborne HFSWR

To investigate the characteristics of sea clutter in monostatic shipborne high-frequency surface-wave radar (HFSWR) with uniform linear motion, the second-order patch scatter cross section is derived. With the previously proposed first-order ocean surface cross section, both cross sections could be reduced to the existing results in land-based HFSWR. Illustrations and discussions are provided on the basis of the simulation results for different situations. Although the spread Bragg lines vary with the ocean surface wind direction, the second-order contributions contaminate the energies of the Bragg lines in the high sea state, which may affect the application of shipborne HFSWR to wind direction extraction from the ratio of the positive and negative Bragg line energies. The derivations and analyses would be of importance to future investigations and applications, not only for target detection but also for ocean remote sensing.

First‐order ocean surface cross‐section for shipborne HFSWR

Onshore high-frequency surface wave radar (HFSWR) has been successfully deployed for early warning and ocean remote sensing. However, there remain potential gaps in ocean remote sensing for shipborne HFSWR. Based on the first-order bistatic field equation, the first-order ocean surface cross-section for shipborne HFSWR is derived, whose validity is verified by experimental results. Consequently, studies on ocean surface information extraction with shipborne HFSWR could be conducted. The simulation results demonstrate that shipborne HFSWR has the potential of sea surface wind direction extraction with a single receiving sensor rather than a receiving array, and in addition wind direction ambiguity existing in onshore HFSWR disappears.

Wind-speed inversion from HF radar first-order backscatter signal

Land-based high-frequency (HF) radars have the unique capability of continuously monitoring ocean surface environments at ranges up to 200 km off the coast. They provide reliable data on ocean surface currents and under slightly stricter conditions can also give information on ocean waves. Although extraction of wind direction is possible, estimation of wind speed poses a challenge. Existing methods estimate wind speed indirectly from the radar derived ocean wave spectrum, which is estimated from the second-order sidebands of the radar Doppler spectrum. The latter is extracted at shorter ranges compared with the first-order signal, thus limiting the method to short distances. Given this limitation, we explore the possibility of deriving wind speed from radar first-order backscatter signal. Two new methods are developed and presented that explore the relationship between wind speed and wave generation at the Bragg frequency matching that of the radar. One of the methods utilizes the absolute energy level of the radar first-order peaks while the second method uses the directional spreading of the wind generated waves at the Bragg frequency. For both methods, artificial neural network analysis is performed to derive the interdependence of the relevant parameters with wind speed. The first method is suitable for application only at single locations where in situ data are available and the network has been trained for while the second method can also be used outside of the training location on any point within the radar coverage area. Both methods require two or more radar sites and information on the radio beam direction. The methods are verified with data collected in Fedje, Norway, and the Ligurian Sea, Italy using beam forming HF WEllen RAdar (WERA) systems operated at 27.68 and 12.5 MHz, respectively. The results show that application of either method requires wind speeds above a minimum value (lower limit). This limit is radar frequency dependent and is 2.5 and 4.0 m/s for 27.68 and 12.5 MHz, respectively. In addition, an upper limit is identified which is caused by wave energy saturation at the Bragg wave frequency. Estimation of this limit took place through an evaluation of a year long database of ocean spectra generated by a numerical model (third generation WAM). It was found to be at 9.0 and 11.0 m/s for 27.68 and 12.5 MHz, respectively. Above this saturation limit, conventional second-order methods have to be applied, which at this range of wind speed no longer suffer from low signal-to-noise ratios. For use in operational systems, a hybrid of first- and second-order methods is recommended.

Wind Direction Extraction from Coastal SAR Images Using Cross-spectral Method

A sensitive processing method for extracting the wind direction from SAR (syn- thetic aperture radar) images is presented. We transfer SAR images to the image spectra using two-dimensional FFT (fast Fourier transform) and take the integral of the image spectra between the wave length 400m and 800m, which is high end containing wind direction signatures in L- band SAR. From this and a reference signal (half or full wave rectifled sinusoidal wave) we obtain the wind direction by using cross-spectral analysis. To examine the potential of our method, a set of simulated images with various SNR (signal to noise ratio) are used. It is shown that, even if the SNR is i30dB, the agreement is to within §3 - . Therefore the processing technique we present allows us the precise estimation of wind direction. We also use 23 JERS-1/SAR scenes from the Tsugaru Strait located between Aomori and Hokkaido prefectures of Japan for the ex- traction of wind direction. The wind directions retrieved from these images have been compared with in situ wind data from the Kikonai, the Tappi and the Ooma lighthouse. Although the lighthouse estimates are lacking in directional resolution (16 direction, 22.5 - interval), the SAR estimates obtained from spectra of 6.4-km-square images are shown to agree with the lighthouse estimates to within a mean absolute error of 21 - . The estimates difierence may be related to the directional resolution of in situ wind data, the SNR of JERS-1/SAR images, the efiect of terrain around the lighthouse, and the ocean current.