Real-Time Ocean Wind Vector Retrieval from Marine Radar Image Sequences Acquired at Grazing Angle

Abstract

This paper proposes a novel algorithm for retrieving the ocean wind vector from marine radar image sequences in real time. It is presented as an alternative to mitigate anemometer problems, such as blockage, shadowing, and turbulence. Since wind modifies the sea surface, the proposed algorithm is based on the dependence of the sea surface backscatter on wind direction and speed. This algorithm retrieves the wind vector using radar measurements in the range of 200-1500 m. Wind directions are retrieved from radar images integrated over time and smoothed (averaged) in space by searching for the maximum radar cross section in azimuth as the radar cross section is largest for upwind directions. Wind speeds are retrieved by an empirical third-order polynomial geophysical model function (GMF), which depends on the range distance in the upwind direction to a preselected intensity level and the intensity level. This GMF is approximated from a dataset of collocated in situ wind speed and radar measurements (;31 000 measurements, ;56 h). The algorithm is validated utilizing wind and radar measurements acquired on the Research Platform (R/P) FLIP (for Floating Instrumentation Platform) during the 13-day Office of Naval Research experiment on HighResolution Air-Sea Interaction (HiRes) in June 2010. Wind speeds ranged from 4 to 22 m s 21 . Once the proposed algorithm is tuned, standard deviations and biases of 148 and 218 for wind directions and of 0.8 and 20.1 m s 21 for wind speeds are observed, respectively. Additional studies of uncertainty and error of the retrieved wind speed are also reported.

Texture-Analysis-Incorporated Wind Parameters Extraction from Rain-Contaminated X-Band Nautical Radar Images

“…This can also be confirmed from the error statistics shown in Table 2. The overall STDs for wind direction and speed shown here are a little higher than the results in [6,[13][14][15]. It should be noted that the results in this paper are based on the analysis of rain-contaminated data only.…”

“…Vicen-Bueno et al [15] proposed an intensity-level-selection (ILS)-based method for deriving wind speed and wind direction from X-band nautical radar images. Although the radar images shown in Figure 1 are in Cartesian coordinate system, the wind algorithm is implemented in the polar coordinate system.…”

“…In [15], the wind direction is chosen as the azimuth along which the maximum of the retrieval range distances vector is located. The wind speed u 0 is assumed to be related to the maximum of the retrieval range distances vector corresponding to the selected intensity level L i as…”

“…However, the relationship between α i and L i is nonlinear, and can be fitted by the third-order polynomial function [15] …”

“…It has been observed that-for a horizontally-polarized (HH-polarized) radar at grazing incidence-the normalized radar cross section (NRCS) is maximum in the upwind direction, but minimum in the crosswind direction [11,12], and it changes exponentially with wind speed [13]. Based on these principles, researchers have developed different X-band nautical radar wind algorithms producing results (as compared to anemometer measurements) with standard deviations (STDs) less than 17.4 • and 1.1 m/s for wind direction and speed [6,[13][14][15], respectively. Among these algorithms, that proposed by Lund et al [6] provides a technique to recognize images contaminated by rain by analyzing the intensity histogram of the images.…”