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>Coupling a Neural Network-Based forward Model and a Bayesian Inversion Approach to Retrieve Wind Field from Spaceborne Polarimetric Radiometers
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Coupling a Neural Network-Based forward Model and a Bayesian Inversion Approach to Retrieve Wind Field from Spaceborne Polarimetric Radiometers
A simulation study to assess the potentiality of sea surface wind vector estimation based on the approximation of the forward model through Neural Networks and on the Bayesian theory of parameter estimation is presented. A polarimetric microwave radiometer has been considered and its observations have been simulated by means of the two scale model. To perform the simulations, the atmospheric and surface parameters have been derived from ECMWF analysis fields. To retrieve wind speed, Minimum Variance (MV) and Maximum Posterior Probability (MAP) criteria have been used while, for wind direction, a Maximum Likelihood (ML) criterion has been exploited. To minimize the cost function of MAP and ML, conventional Gradient Descent method, as well as Simulated Annealing optimization technique, have been employed. Results have shown that the standard deviation of the wind speed retrieval error is approximately 1.1 m/s for the best estimator. As for the wind direction, the standard deviation of the estimation error is less than 13° for wind speeds larger than 6 m/s. For lower wind velocities, the wind direction signal is too weak to ensure reliable retrievals. A method to deal with the non-uniqueness of the wind direction solution has been also developed. A test on a case study has yielded encouraging results.
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机译：基于神经网络的正演模型逼近和参数估计的贝叶斯理论，提出了评估海面风矢量估计潜力的模拟研究。已经考虑了极化微波辐射计，并且已经通过两尺度模型模拟了其观测。为了执行模拟，已从ECMWF分析字段中得出了大气和地面参数。为了检索风速，已使用最小方差（MV）和最大后验概率（MAP）标准，而对于风向，已利用最大似然（ML）标准。为了最小化MAP和ML的成本函数，已采用常规的梯度下降方法以及模拟退火优化技术。结果表明，对于最佳估算器，风速恢复误差的标准偏差约为1.1 m / s。对于风向，风速大于6 m / s时，估计误差的标准偏差小于13°。对于较低的风速，风向信号太弱，无法确保可靠的取回。还开发了一种解决风向解的非唯一性的方法。案例研究的测试产生了令人鼓舞的结果。
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