Relating snow radar retrievable parameters to snow microstructure for hydrological forecasting applications
PI: Kevin Hammonds
PhD Student: Chris Donahue
Funding Source: NASA
Although terrestrial snowcovers contribute to the global water supply of nearly one-sixth of the world’s population, most of the world’s snow cover is located in remote and isolated terrain. To monitor the state of both seasonal and perennial snow covers, several snow monitoring systems based on microwave remote sensing techniques have been deployed, but a remote sensing product that derives the snow water equivalent over various types of terrain and on a global scale, has yet to be developed. In this research, the microstructural properties of snow that modify the radar reflectivity, polarimetric signature, and image coherence of multi-frequency and synthetic aperture radar systems will be investigated primarily using low power and portable ultra wideband radar systems from Flat Earth Sensing Solutions. From within the SRL, a series of controlled laboratory experiments will be performed in which artificially created snowpacks are perturbed to mimic natural-like conditions while being monitored with a full suite of remote sensing instrumentation, including multi- frequency polarimetric radar and broadband radiometry systems. To compliment these observations, advanced materials characterization techniques will also be employed, where X-ray computed microtomography, scanning electron microscopy, and electron backscatter diffraction will be used to elucidate micron and sub-micron scale snow properties such as the crystallographic orientation and the degree of anisotropy of the snow grains. With this unique combination of remote sensing and in-situ observations from within a controlled laboratory environment, new insight into the micro-scale physics of snow that drive macro-scale radar- and radiometric- observables will be derived, microwave remote sensing retrieval algorithms will be improved, and several novel approaches to the remote sensing of snow will be applied and tested.
Developing Real-Time Data Processing and Analytics for Avalanche Forecasting Using FMCW Lidar
PI: Kevin Hammonds, Sean Yaw
M.S. Students: James Dillon (Civil Engineering), Peter Ottsen (Computer Science)
Funding Source: USDOT Transportation Avalanche Research Pool (TARP)
Terrestrial scanning lidar systems (TLS) have recently been shown to enhance avalanche forecasting and mitigation efforts by aiding forecasters in remotely analyzing snowpack depths over complex terrain (Deems et al. 2013; Deems et al. 2015; Prokop 2008). In this reserach, a detailed laboratory study in which both long-range and short-range TLS systems from Blackmore Sensors and Analytics Inc. will be brought into the SRL and tested with different types of snowpacks, snow surfaces, and precipitation rates. The primary advantage of a frequency modulated continous wave (FMCW) TLS is its ability to detect moving targets, such as snowflakes, that can then be distinguished from the background scene, allowing for an evaluation of snow depth during precipitation or wind events. These tests will inform what can be expected in the field and to what degree other parameters of interest can be derived, including precipitation rate and slope angle. Novel analysis algorithms and software will be developed to support the use of TLS data to better inform avalanche risk assessment and decision making. Avalanche risk decision support will be driven by the extraction and aggregation of features from TLS data (e.g. snow depth, slope angles, surface properties). Algorithms will be developed that will determine these properties from streaming TLS data, such that field-users can access this data in real-time. Initial field measurements will be made at the Big Sky and Bridger Bowl ski areas, before being deploying to TARP avalanche forecasting zones for additional testing.