Publications

Abstract not provided.

Abstract not provided.

Understanding the lightning science behind the lightning detected by remote sensing systems is crucial to Sandia’s remote sensing program. Improved understanding of lightning properties can lead to improvements of onboard and/or ground-based background signal discrimination.

Abstract not provided.

Among the background signals commonly seen by Earth-monitoring satellites is the specular reflection of sunlight off of Earth's surface, commonly referred to as a glint. This phenomenon, involving liquid or ice surfaces, can result in the brief, intense illumination of satellite sensors appearing from the satellite perspective to be of terrestrial origin. These glints are important background signals to be able to identify with confidence, particularly in the context of analyzing data from satellites monitoring for transient surface or atmospheric events. Here we describe methods for identifying glints based on the physical processes involved in their production, including spectral fitting and polarization measurements. We then describe a tool that, using the WGS84 spheroidal Earth model, finds the latitude and longitude on Earth where a reflection of this type could be produced, given input Sun and satellite coordinates. This tool enables the user to determine if the surface at the solution latitude and longitude is in fact reflective, thus identifying the sensor response as a true glint or an event requiring further analysis.

To assess the influence of mountain-scale thermal property model variations on predicted host-rock thermal response, a series of heat conduction calculations were run using a representative two-dimensional cross section of Yucca Mountain. The effects of modeled geologic structure were evaluated through comparisons of results from a single-material, homogeneous model with those from a uniformly layered model, a discontinuous sloping-layered model, and a geo-statistical realization of thermal properties. Comparisons indicate that assumed geologic structure can result in up to a 24{degrees}C difference in predicted temperature response. Further, thermal simulations of the method used to analyze geostatistical realizations of thermal properties shows promise as an efficient means of capturing geologic structure without the complexities of intricate finite element meshing. The functional representation of two thermal property models were also investigated. The first examines the effect of using a weighting scheme to define properties for a single, homogenous material model. The second investigates the impact of thermal property temperature dependence on predicted response. As with the investigation of geologic structure, noticeable differences in predicted temperatures (up to 29{degrees}C) were found to result.