Abstract
The variational model inversion (VAR) method for synthetic aperture radar (SAR) wind retrieval based on Bayesian theory can overcome the limitation of the traditional wind streak algorithm by introducing background wind and considering all sources of error, but its optimal solution is unstable and the time latency is long. In this paper, we propose a new wind retrieval method by applying optimal interpolation (OI) theory to construct a formula that considers the SAR information, background information coming from the numerical prediction model, and their associated wellcharacterized errors. The retrieved wind vector can be acquired by the analytic solution of the OI formula. Experimental results of the simulation data and Sentinel-1 SAR data show that the OI wind retrieval method can effectively reduce the background wind error and is more sensitive to wind speed than wind direction. Compared with other methods, the accuracy of the OI method is similar to that of the VAR method, but significantly higher than that of the direct wind retrieval (DIRECT) method. The time latency of the OI method is the shortest, and the calculation efficiency is much higher than that of the VAR method. The OI method can be effectively applied to SAR wind retrieval and has unique advantages.Sea surface wind is a crucial parameter for studying the physical quantity of the sea surface and plays an important role in many fields such as weather forecasts [1, 2], wind energy resource management [3,4], wave numerical simulation [5,6], and oil spill monitoring [7,8]. Because of the limited spatial and temporal coverage, high-precision sea surface wind acquired from buoys, ships and offshore platforms are not meeting the growing demand [9].In recent decades, with the development of satellite remote sensing, the technology of acquiring sea surface wind using satellite sensor detection data has gradually matured and improved. Among various satellite sensors, microwave radiometers and scatterometers play an important role in providing global sea surface wind. However, microwave radiometers and scatterometers can acquire only low spatial resolution (12.5-50 km) sea surface wind. This relatively low spatial resolution is
Highlights
In 2002, Portabella et al (2002) proposed a sea surface wind inversion method based on the Bayesian theory, which consists of estimating the wind from an synthetic aperture radar (SAR) normalized radar cross section (NRCS) measurement, a geophysical model function (GMF) model, a prior wind from the numerical prediction model, and their associated uncertainties
The maximum VOE and VVE are less than 2 m/s, while the maximum VDE is substantially greater than 2 m/s (Figures 7A,C,E). φOE and the variational model inversion (VAR) retrieved wind direction errors φVE are basically consistent with φBE, and both have no obvious improvement with respect to φBE (Figures 7B,D,F)
We propose a new SAR sea surface wind retrieval method called the optimal interpolation (OI) wind retrieval method
Summary
Sea surface wind is a crucial parameter for studying the physical quantity of the sea surface and plays an important role in many fields such as weather forecasts (Von Ahn et al, 2006; Friedman et al, 2010), wind energy resource management (Hasager et al, 2011; Chang et al, 2015), wave numerical simulation (Cavaleri et al, 2007; Sullivan and McWilliams, 2010), and oil spill monitoring (Espedal, 1999; Cheng et al, 2014). In 2002, Portabella et al (2002) proposed a sea surface wind inversion method based on the Bayesian theory, which consists of estimating the wind from an SAR NRCS measurement, a GMF model, a prior wind from the numerical prediction model, and their associated uncertainties This approach constructs a variational formulation, in which the optimum wind vector is determined by minimizing a cost function. Case Involving Adding Regular Errors to Different Background Wind Given VT ∈ [5 m/s, 28 m/s] with a step size of 1 m/s, and φT ∈ [0°, 360°) with a step size of 5°, 1,728 simulated observations are obtained from the true wind vector by the CMOD5 GMF. The RMSE of the retrieved wind direction is maintained at 19° under different background wind error conditions, and is smaller than φOE. Case with Varying Incidence Angles To comprehensively test the performance of the OI method, we adopt incidence angles from 20° to 47°(ESA
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