Inadequate representation of the environment is a limitation for prediction of radar system performance as well as for validation of propagation codes. To improve understanding of how different environmental effects/parameters compete and compare, this study examines the sensitivity of radar wave propagation to a suite of environmental parameters for low grazing angle near‐surface radar systems at 3–15 GHz at horizontal and vertical polarizations. A global sensitivity analysis method is used, which accounts for parameter interactions, and propagation is modeled using the parabolic equation method. Environmental parameters examined include eight sea state parameters and eight parameters characterizing the vertical structure and character of range‐independent refractivity profiles. The relative importance of parameters varies more with frequency than polarization, and parameter interactions are found to be significant. Atmospheric mixed layer parameters are found to be the most sensitive, particularly the thickness of the mixed layer. The most significant ocean surface parameter is swell period, although sea directionality is important at 3 GHz and sea surface roughness and salinity are important at 9 and 15 GHz. Because of the spatial variability of sensitivity throughout the domain, regional analysis is performed to determine the most important parameters in different regions of the domain (1000 m in altitude and 60 km in range). These regional sensitivity results, along with those for the whole domain, provide guidance on prioritization of environmental characterization in numerical weather prediction and inversion studies (e.g., refractivity from clutter studies), which are two common methods currently used to address environmental effects on propagation.

Radar performance is impacted by variations in atmospheric refractivity because it changes the direction of electromagnetic wave propagation. Because direct measurement and simulation of atmospheric refractivity at high resolution over large distances is challenging, inversion techniques have developed to address this knowledge gap in predicting refractivity. Here we use synthetic radar data to solve refractivity inversion problems, focusing on evaporation ducts. Unlike most prior “refractivity from clutter” studies, this study examines inversions based on point‐to‐point propagation rather than sea clutter. This approach removes complexities associated with uncertainties in surface reflection coefficients; however, the sea surface still plays a role in the forward scattering and reflection (multipath) of electromagnetic waves. Using synthetic data that permit quantitative evaluation of inverse solution accuracy and detailed knowledge regarding the sea state conditions used to produce the data, we evaluate the impact various representations of the sea surface have on the accuracy of refractivity inversions. These representations include neglect of the sea surface, use of the same phase‐resolved surface, and use of a statistically equivalent sea surface. Results show that neglect of the sea surface and use of a statistically equivalent sea surface significantly decreases the accuracy of inverse solutions, and this decrease primarily results from discrepancies in the multipath pattern that generate larger variations in the propagation than does the refractivity. We show that averaging propagation over several different phases of the sea surface aids in washing out the multipath pattern to increase sensitivity of the inversion to the features of the refractivity profile.

[1] In situ particle image velocimetry measurements, at a resolution of 3.5 Kolmogorov scales, have been performed in the inner part of the coastal bottom boundary layer. The spatial details enable us to directly determine the vertical distributions of mean velocity, Reynolds shear stress, shear production and dissipation rates, energy spectra, and abundance of eddies. Focusing on cases with wave velocity of similar magnitude as the mean current, velocity profiles have logarithmic distributions in the upper half of the sample area. Below the log layer, but well above the bottom ripples, an inflection point appears, indicating a region of flow instability. Based on data interpretation, which includes variations in wave phase with height, this inflection occurs near the interface between current and thinner wave boundary layer (WBL) below it. Scaling of mean velocity profiles with shear velocity and characteristic roughness is effective only above the inflection point, while turbulence parameters scale reasonably well at all elevations. Instabilities associated with the inflection are manifested by a peak in turbulent shear production rate and a rapid increase in small-scale turbulence, as is evident from trends of the dissipation rate, energy spectra, and distribution of eddies with elevation. Therefore, the presence of a WBL generates a shear production peak and rapid increase in the dissipation rate at higher elevations than those found in rough-wall steady boundary layers. Transition between current and wave boundary layers is also characterized by broad Reynolds stress peaks and shear production exceeding the dissipation rate.Citation: Hackett, E. E., L. Luznik, A. R. Nayak, J. Katz, and T. R. Osborn (2011), Field measurements of turbulence at an unstable interface between current and wave bottom boundary layers,

To assess a radar system's instantaneous performance on any given day, detailed knowledge of the meteorological conditions is required due to the dependency of atmospheric refractivity on thermodynamic properties such as temperature, water vapor, and pressure. Because of the significant challenges involved in obtaining these data, recent efforts have focused on development of methods to obtain the refractivity structure inversely using radar measurements and radar wave propagation models. Such inversion techniques generally use simplified refractivity models in order to reduce the parameter space of the solution. Here the accuracy of three simple refractivity models is examined for the case of an evaporation duct. The models utilize the basic log linear shape classically associated with evaporation ducts, but each model depends on various parameters that affect different aspects of the profile, such as its shape and duct height. The model parameters are optimized using radiosonde data, and their performance is compared to these atmospheric measurements. The optimized models and data are also used to predict propagation using a parabolic equation code with the refractivity prescribed by the models and measured data, and the resulting propagation patterns are compared. The results of this study suggest that the best log linear model formulation for an inversion problem would be a two‐layer model that contains at least three parameters: duct height, duct curvature, and mixed layer slope. This functional form permits a reasonably accurate fit to atmospheric measurements as well as embodies key features of the profile required for correct propagation prediction with as few parameters as possible.