The estimation of lower refractivity uncertainty from radar sea clutter using the Bayesian-MCMC method |
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Authors: | Sheng Zheng |
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Affiliation: | Sheng Zheng College of Meteorology and Oceangraphy,PLA University of Science and Technology,Nanjing 211101,China |
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Abstract: | The estimation of lower atmospheric refractivity from radar sea clutter(RFC) is a complicated nonlinear optimization problem.This paper deals with the RFC problem in a Bayesian framework.It uses the unbiased Markov Chain Monte Carlo(MCMC) sampling technique,which can provide accurate posterior probability distributions of the estimated refractivity parameters by using an electromagnetic split-step fast Fourier transform terrain parabolic equation propagation model within a Bayesian inversion framework.In contrast to the global optimization algorithm,the Bayesian-MCMC can obtain not only the approximate solutions,but also the probability distributions of the solutions,that is,uncertainty analyses of solutions.The Bayesian-MCMC algorithm is implemented on the simulation radar sea-clutter data and the real radar seaclutter data.Reference data are assumed to be simulation data and refractivity profiles are obtained using a helicopter.The inversion algorithm is assessed(i) by comparing the estimated refractivity profiles from the assumed simulation and the helicopter sounding data;(ii) the one-dimensional(1D) and two-dimensional(2D) posterior probability distribution of solutions. |
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Keywords: | refractivity from clutter terrain parabolic equation propagation model Bayesian-Markov chain Monte Carlo uncertainty analysis |
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