Optimal Interpolation Model for Synthetic Aperture Radar Wind Retrieval

Abstract

The variational model inversion (VAR) method for synthetic aperture radar (SAR) wind retrieval based on the Bayesian theory can overcome the limitations 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 article, we propose a new wind retrieval method by applying the optimal interpolation (OI) theory to construct a formula that considers the SAR information, background information coming from the numerical prediction model, and their associated well-characterized errors. The retrieved wind vector can be acquired by the analytic solution of the OI formula. The results from the simulation data show that the error of the OI-retrieved wind is smaller than that of background wind in all considered cases; in particular, the accuracy of the OI-retrieved wind speed is significantly improved. Experiments on the Sentinel-1 SAR data show that the root mean square error of the OI-retrieved wind speed and direction are 1.4 m/s and 35°, respectively. Compared with other methods, the retrieved wind speed accuracy of the OI method is similar to that of the VAR method but higher than that of the direct wind retrieval 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 results indicate that the OI method can be effectively applied to SAR wind retrieval.

A Review for Retrieving Wind Fields by Spaceborne Synthetic Aperture Radar

“…The WD inversion for the SAR image data is based on the wind streak features in the SAR data. Due to the complexity of the SAR imaging and the variability of the sea and air environment, not all of the SAR images contain clear wind streak features [118,119]. There are certain limitations in obtaining the WD with this method [64,120].…”

“…In WS inversion, Jones et al used SEASAT SAR data and found that there was a certain relationship between the normalised radar cross-section (NRCS) of the SAR and WS [11]. Then, the geophysical model function (GMF) based on the NRCS of copolarized SAR was proposed and continually updated to form a series of empirical models [12,13]. However, the phenomenon of saturation exists in WS inversion with copolarized SAR when the WS exceeds 25 m/s [14][15][16].…”

Assessment of Offshore Wind Resources, Based on Improved Particle Swarm Optimization

“…However, the accuracy of LSTM may be compromised with the inclusion of exogenous features in the training sets and the duration of the prediction ahead. Back propagation (BP) [20][21][22][23] and improved particle swarm optimization (IPSO) [24][25][26][27] have been widely used in the wind energy resource evaluation, but the BP neural network (NN) has the shortcomings of a local minimization and a slow convergence speed. Combining the BPNN model with the PSO algorithm can effectively avoid the local optimal problem [28,29].…”