Emerging application areas such as air pollution in megacities, wind energy, urban security, and operation of unmanned aerial vehicles have intensified scientific and societal interest in mountain meteorology. To address scientific needs and help improve the prediction of mountain weather, the U.S. Department of Defense has funded a research effort—the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program—that draws the expertise of a multidisciplinary, multi-institutional, and multinational group of researchers. The program has four principal thrusts, encompassing modeling, experimental, technology, and parameterization components, directed at diagnosing model deficiencies and critical knowledge gaps, conducting experimental studies, and developing tools for model improvements. The access to the Granite Mountain Atmospheric Sciences Testbed of the U.S. Army Dugway Proving Ground, as well as to a suite of conventional and novel high-end airborne and surface measurement platforms, has provided an unprecedented opportunity to investigate phenomena of time scales from a few seconds to a few days, covering spatial extents of tens of kilometers down to millimeters. This article provides an overview of the MATERHORN and a glimpse at its initial findings. Orographic forcing creates a multitude of time-dependent submesoscale phenomena that contribute to the variability of mountain weather at mesoscale. The nexus of predictions by mesoscale model ensembles and observations are described, identifying opportunities for further improvements in mountain weather forecasting.

No abstract

In this paper a novel algorithm for distant aircraft detection for visual sense-and-avoid for UAV is presented. The algorithm uses local edge density to partition the frame into two types of regions. The first type is the unstructured or homogeneous part like sky region and the second part where there is a structured background, like high contrast clouds or terrain regions. The airplanes are detected on the two types of regions with different strategies. The algorithm was planned to run in an embedded environment with low power consumption, thus it can be run onboard of a small or mid-size UAV. First steps towards the GPU implementation on the nVidia Jeston TK1 development board are done and also presented in the paper.

No abstract