Idealized modeling of the role of stability and shear on mesoscale gravity wave evolution

Mesoscale Gravity Waves (MGWs) are large pressure perturbations that form in the presence of a stable layer at the surface either behind Mesoscale Convective Systems (MCSs) in summer or over warm frontal surfaces behind elevated convection in winter. MGWs are associated with damaging winds, moderate to heavy precipitation, and occasional heat bursts at the surface. The forcing mechanism for MGWs in this study is hypothesized to be evaporative cooling occurring behind a convective line. This evaporatively-cooled air generates a downdraft that then depresses the surface-based stable layer and causes pressure decreases, strong wind speeds and MGW genesis. Using the Weather Research and Forecast Model (WRF) version 3.0, evaporative cooling is simulated using an imposed cold thermal. Sensitivity studies examine the response of MGW structure to different thermal and shear profiles where the strength and depth of the inversion are varied, as well as the amount of wind shear. MGWs are characterized in terms of response variables, such as wind speed perturbations (U'), temperature perturbations (T'), pressure perturbations (P'), potential temperature perturbations (Θ'), and the correlation coefficient (R) between U' and P'. Regime Diagrams portray the response of MGW to the above variables in order to better understand the formation, causes, and intensity of MGWs. The results of this study indicate that shallow, weak surface layers coupled with deep, neutral layers above favor the formation of waves of elevation. Conversely, deep strong surface layers coupled with deep, neutral layers above favor the formation of waves of depression. This is also the type of atmospheric setup that tends to produce substantial surface heating at the surface.

Three-dimensional, in-silico breast phantom for multimodality image simulations

We report on the construction of anatomically realistic three-dimensional in-silico breast phantoms with adjustable sizes, shapes and morphologic features. The concept of multiscale spatial resolution is implemented for generating breast tissue images from multiple modalities. Breast epidermal boundary and subcutaneous fat layer is generated by fitting an ellipsoid and 2nd degree polynomials to reconstructive surgical data and ultrasound imaging data. Intraglandular fat is simulated by randomly distributing and orienting adipose ellipsoids within a fibrous region immediately within the dermal layer. Cooper’s ligaments are simulated as fibrous ellipsoidal shells distributed within the subcutaneous fat layer. Individual ductal lobes are simulated following a random binary tree model which is generated based upon probabilistic branching conditions described by ramification matrices, as originally proposed by Bakic et al [3, 4]. The complete ductal structure of the breast is simulated from multiple lobes that extend from the base of the nipple and branch towards the chest wall. As lobe branching progresses, branches are reduced in height and radius and terminal branches are capped with spherical lobular clusters. Biophysical parameters are mapped onto the complete anatomical model and synthetic multimodal images (Mammography, Ultrasound, CT) are generated for phantoms of different adipose percentages (40%, 50%, 60%, and 70%) and are analytically compared with clinical examples. Results demonstrate that the in-silico breast phantom has applications in imaging performance evaluation and, specifically, great utility for solving image registration issues in multimodality imaging.