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Refractivity-from-clutter (RFC) is a technique for remote sensing of the lowest portion of vertical atmospheric refractivity profile, particularly evaporation and surface-based ducts. It uses the sea-surface backscattered radar signal to estimate the ducting conditions. The Lower Atmospheric Propagation (LATPROP) radar is a software-defined, coherent-on-receive remote sensing system built on a low-cost, commercial off-the-shelf marine radar platform operating at 9.41 GHz. The system is designed to specifically operate as a RFC-capable platform sensitive to low clutter power returns from the sea surface. This paper examines the appropriate radar design, system modifications, and radar signal processing steps, and provides a comparison with existing RFC-capable systems. The LATPROP system was deployed during the Coupled Air-Sea Processes and electromagnetic Ducting Research West Campaign that took place off of the coast of Southern California during October 2017 and was shown to accurately capture the effects of the ducting conditions. [ABSTRACT FROM AUTHOR]

The refractivity from clutter (RFC) technique has been proved to be an effective way to estimate atmospheric duct structure. An important issue for RFC is how to make the estimate more robust, especially in range-dependent ducting conditions. Traditionally, statistical inversion methods need a large number of forward propagation model runs to obtain an acceptable result. Especially when the parameter search space is multidimensional, these methods are prone to being trapped into local optimal solutions. Recently published results (Zhao and Huang) indicate that the adjoint parabolic equation (PE) method holds promise for real-time estimation of one-dimensional refractive index structure from radar sea clutter returns. This paper is aimed at extending the adjoint PE method to range-dependent evaporation duct cases, with a log-linear relationship describing duct structures. Numerical simulations are used to test the performance of this method and the results are compared with that retrieved using a genetic algorithm. Both noise-free and 3-dB additive Gaussian noise clutter simulations are considered, as well as linearly and nonlinearly varying duct height with range. [ABSTRACT FROM AUTHOR]

Radio wave propagation on low-altitude paths over the ocean above 2 GHz is significantly affected by negative refractivity gradients in the atmospheric surface layer, which form what is often referred to as an evaporation duct (ED). Refractivity from clutter (RFC) is an inversion approach for the estimation of the refractivity profile from radar clutter, and RFC-ED refers to its implementation for the case of evaporation ducts. An approach for fusing RFC-ED output with evaporation duct characterization that is based on ensemble forecasts from a numerical weather prediction (NWP) model is examined here. Three conditions of air-sea temperature difference (ASTD) are examined. Synthetic radar clutter observations are generated using the Advanced Propagation Model. The impacts of ASTD on the evaporation duct refractivity profile, atmospheric parameter inversion, and propagation factor distributions are studied. Relative humidity at a reference height and ASTD are identified as state variables. Probability densities from NWP ensembles, RFC-ED, and joint inversions are compared. It is demonstrated that characterization of the near-surface atmosphere by combining RFC-ED and NWP reduces the estimation uncertainty of ASTD and relative humidity in an evaporation duct, with respect to using either method alone. [ABSTRACT FROM AUTHOR]

This paper describes a Markov chain Monte Carlo (MCMC) sampling approach for the estimation of not only the radio refractivity profiles from radar clutter but also the uncertainties in these estimates. This is done by treating the refractivity from clutter (RFC) problem in a Bayesian framework. It uses unbiased MCMC sampling techniques, such as Metropolis and Gibbs sampling algorithms, to gather more accurate information about the uncertainties. Application of these sampling techniques using an electromagnetic split-step fast Fourier transform parabolic equation propagation model within a Bayesian inversion framework can provide accurate posterior probability distributions of the estimated refractivity parameters. Then these distributions can be used to estimate the uncertainties in the parameters of interest. Two different MCMC samplers (Metropolis and Gibbs) are analyzed and the results compared not only with the exhaustive search results but also with the genetic algorithm results and helicopter refractivity profile measurements. Although it is slower than global optimizers, the probability densities obtained by this method are closer to the true distributions. [ABSTRACT FROM AUTHOR]