Civil and Environmental Engineering publications and data sets
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Item Infrared Quantitative Imaging Velocimetry (IR-QIV) data supporting Schweitzer & Cowen, 2021, WRRSchweitzer, Seth; Cowen, Edwin A. (Todd) (2021-07-07)This data set contains infrared images and velocity measurements collected at two sites on the Sacramento River, and one of its tributaries, near Sacramento, California, USA, in November 2017. Details on the data collection and analysis are available in Schweitzer & Cowen, 2021, WRR. Included are infrared images of the flowing water surface, the instantaneous velocity field as calcalated from these images using Infrared Quantitative Image Velocimetry (IR-QIV), a near-field remote sensing method of surface velocimetry similar to LSPIV that is capable of measuring the instantaneous two-dimensional velocity field and extract metrics of turbulence. Also included are concurrent measurements of water velocity using traditional acoustic instruments (ADV and ADCP), and meteorological measurements made on site.Item Postprocessed large-eddy simulation data from: Connection between mass flux transport and eddy diffusivity in convective atmospheric boundary layersLi, Qi; Cheng, Yu; Gentine, Pierre (2021-05-06)Turbulence parameterizations for convective boundary layer in coarse-scale atmospheric models usually consider a combination of the eddy-diffusive transport and a non-local transport, typically in the form of a mass flux term, such as the widely adopted eddy-diffusivity mass-flux (EDMF) approach. These two types of turbulent transport are generally considered to be independent of each other. Using results from large-eddy simulations, here we show that a Taylor series expansion of the updraft and downdraft mass-flux transport can be used to approximate the eddy-diffusivity transport in the atmospheric surface layer and the lower part of the mixed layer, connecting both eddy-diffusivity and mass-flux transport theories in convective conditions, which also quantifies departure from the Monin-Obukhov similarity in the surface layer. The study provides a theoretical support for a unified EDMF parameterization applied to both the surface layer and mixed layer and highlights important correction required for surface models relying on Monin-Obukhov similarity. This dataset supports that study.Item Code and data from: Gaussian Process Regression for Estimating EM Ducting Within the Marine Atmospheric Boundary LayerSit, Hilarie; Earls, Christopher J (2019-11-26)We show that Gaussian process regression (GPR) can be used to infer the electromagnetic (EM) duct height within the marine atmospheric boundary layer (MABL) from sparsely sampled propagation factors within the context of bistaticradars. These propagation factors are simulated using PETOOL, developed by Ozgun et al. 2011, and the datasets for the three cases that correspond to the different sparse sampling techniques can be found in the data folder. We use GPR to calculate the posterior predictive distribution on the labels (i.e. duct height) from both noise-free and noise-contaminated array of propagation factors. For duct height inference from noise-contaminated propagation factors, we compare a naive approach, utilizing one random sample from the input distribution (i.e. disregarding the input noise), with an inverse-variance weighted approach, utilizing a few random samples to estimate the true predictive distribution. The resulting posterior predictive distributions from these two approaches are compared to a "ground truth" distribution, which is approximated using a large number of Monte-Carlo samples. We use Python 3.6.4 and scikit-learn 0.20.2. The ability of GPR to yield accurate duct height predictions using few training examples, along with its inference speed, indicates the suitability of the proposed method for real-time applications. This is the dataset and code that supports this work.Item Code and data from: Characterizing Evaporation Ducts Within the Marine Atmospheric Boundary Layer Using Artificial Neural NetworksSit, Hilarie; Earls, Christopher J (2019-10-22)Abstract: We apply a multilayer perceptron machine learning (ML) regression approach to infer electromagnetic (EM) duct heights within the marine atmospheric boundary layer (MABL) using sparsely sampled EM propagation data obtained within a bistatic context. EM propagation data is simulated using PETOOL, a MATLAB-based software developed by Ozgun et al. 2011 for solving the split-step parabolic equation approximation of Helmholtz wave equation. Three cases in the data folder correspond to different sparse sampling techniques detailed in our paper. Artificial neural networks are implemented utilizing Tensorflow, and its hyperparameters are selected with grid search. Results for model selection and evaluation can be found in their respective folders. The resulting speed of our ML predictions of EM duct heights, using sparse data measurements within MABL, indicates the suitability of the proposed method for real-time applications.Item Reflections and the focusing effect from an ideal three-dimensional rough surfaceClavano, Wilhelmina R.; Philpot, William D. (2006-03-04T15:33:50Z)An analytical expression for higher-order reflectances from a shallow-water homogeneous ocean bottom modeled as an egg-carton surface is presented. Roughness of this ideal surface is expressed as the amplitude-to-length ratio of its basic sinusoidal function. Any real surface that can be approximated by an egg-carton function will effectively have a comparable roughness metric. Incidence and reflection directions are considered in full azimuthal variation. The detector is located just below the water surface so that only in-water reflections are considered and there are no air-water transmission effects. Furthermore, this setup allows for an understanding of reflections that occur in media with any index of refraction or absorption coefficient. Fixing the detector footprint but adjusting its field-of-view enables the observation of the same bottom surface area as the depth varies while keeping the roughness and the number of waveforms viewed constant. First-order reflectance decreases as the roughness increases, as was shown in the two-dimensional case. This is true as the roughness varies, regardless of the bottom reference level chosen. Focusing effects are expected from (but are not limited to) second-order reflectance and are due to parts of the bottom whose angles maximize both incoming light and the reflections toward the detector. Along a plane about the vertical axis, the roughness ratio for a fixed-length waveform that returns the highest reflectance can be found. In three dimensions, this phenomenon is complicated by reflections from all hemispherical directions. Shadowing and obscuration behave similarly as in the two-dimensional case although shadowed areas will have an increased potential to reflect light from other directions (than the plane defined by the source incidence and the vertical directions). This is expected to cause higher order reflections to increase as the roughness increases.Item The off-specular peak and polarisation effects of an undulating underwater sufaceClavano, Wilhelmina R.; Philpot, William D. (2006-03-03T18:45:20Z)Periodic undulations are used to describe underwater bottom roughness. An expression of the bi-directional reflectance distribution function (BRDF) is given that is dependent on the given roughness metric. Highlights include an off-specular peak and polarisation effects. For an undulating underwater surface, we have shown through geometric optics that reflectance from a rough diffuse surface increases as the viewing direction approaches the backward direction even in the absence of shadowing and/or self-shading (Clavano & Philpot (2003), see also Cox & Munk (1956)). The effects of shadowing and self-shading are equivalent to applying a geometrical attenuation factor to specular reflectance, which is similar to an analysis of morphological effects using triangular waves by Zaneveld & Boss (2003). We show that a reflectance peak displaced away from the specular direction occurs at large angles of incidence (relative to the global normal) as the surface gets rougher (part of work in Clavano & Philpot (2004)). Similar results have been shown for oil films on ocean surfaces using Monte Carlo methods by Otremba & Piskozub (2004) and Otremba (2004). As a general result, an expression of the full bi-directional reflectance distribution function (BRDF) is given. While geometrical effects play a significant role in the reflectance distribution, we consider polarisation effects (as in Mullamaa (1962, 1964)) to gain more insight into real-world reflectances and compare with empirical distributions described by Cox & Munk (1956).Item Backscattering anisotropy near $180^{\circ}$: an indication of particle size and shapeClavano, Wilhelmina R.; Boss, Emmanuel; Agrawal, Yogesh C. (2006-03-01T18:45:55Z)By modelling the single scattering of particles in the exact backward direction ($180^{\circ}$) and $5^{\circ}$ around, the field of view of an instrument measuring backscattering is simulated. Calculations of the scattering Mueller matrix $M_{ij}$ using a development of the extended boundary condition method [1] are made for spheroidal particles with sizes ($D$ in $\mu$), shapes (defined by spheroidal aspect ratio $\frac{s}{t}$) and refractive indices similar to ($m = 1.05 + 0.01i$) marine particles found in the natural environment. Results show that information about size and shape can be gathered from the intensity patterns of the backscattering for particles within the anomalous diffraction region. Comparison between the polarised scattering intensity patterns ($I_{\parallel}$ and $I_{\perp}$) produced by these non-spheres and their volume-equivalent spheres provides insight into the information available from backscattering polarimetry on the effects of size and shape in light scattering by differently shaped particles.