A Decision Support System for Operational Forecasting of New Snow Natural Avalanches
Predicting new-snow or storm-snow avalanches to a high degree of accuracy (location, timing, and size) is important for state departments of transportation (DOTs) for minimizing highway closure times and ensuring safe transport in the avalanche terrain. New-snow avalanches arise because of instabilities in freshly fallen snow layers or at the interface of new and old snow. While in situ observations are critical to understanding the current state of the snowpack and making important decisions, spatial and temporal variabilities in mountainous terrain make it difficult to extrapolate the few observations that are typically available. Furthermore, highway avalanche forecasters do not have a way of remotely observing snow strength in different layers of new snow and forecasting snow stability based on this information. Previously, this information could be obtained only by human sampling in the field, which can be hazardous. For NCHRP 20-30/IDEA 263, the research team will develop an avalanche forecasting system that combines numerical weather prediction; machine learning, advanced real-time weather observations; and a simple-to-interpret, physics-based, snowpack stability tool. The proposed system will provide early warning information on storm-snow instability that is too dangerous to obtain by sampling snow manually during storms. A device developed by the research team, the Differential Emissivity Imaging Disdrometer will be used to characterize individual falling snowflakes (e.g., crystal type, mass, density, precipitation rate) in real-time along with other systems. Individual snowflake data from the device will be integrated into a computationally lightweight, real-time, snow-stability model (SNOSS-ANT) that accounts for both shear and anti-crack modes of failure in new snow. This lightweight model will be combined with a simple machine-learning routine developed for the National Oceanic and Atmospheric Administration’s High-Resolution Rapid Refresh weather forecasting model to forecast the timing of natural new-snow avalanches. Utah DOT will help the research team evaluate and demonstrate the developed system at its Atwater field site on State Route 210. The proposed innovation addresses highway operations by (1) deploying improved or advanced technologies for systems operations, (2) incorporating reliability estimation into planning and operations modeling tools, and (3) real-time data fusion to support traveler information systems. In addition, the proposed tool will improve highway and worker safety through (1) new automated identification and warning of hazardous conditions, (2) advanced technology to reduce highway workers’ exposure to hazardous conditions, and (3) warning of impending hazards.
Language
- English
Project
- Status: Active
- Funding: $140,000.00
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Contract Numbers:
Project 20-30, IDEA 263
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Sponsor Organizations:
National Cooperative Highway Research Program
Transportation Research Board
500 Fifth Street, NW
Washington, DC United States 20001American Association of State Highway and Transportation Officials (AASHTO)
444 North Capitol Street, NW
Washington, DC United States 20001Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC United States 20590 -
Project Managers:
Jawed, Inam
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Performing Organizations:
University of Utah
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Principal Investigators:
Pardyjak, Eric
- Start Date: 20240101
- Expected Completion Date: 0
- Actual Completion Date: 0
Subject/Index Terms
- TRT Terms: Avalanches; Decision support systems; Machine learning; Snow; Warning systems; Weather forecasting
- Subject Areas: Data and Information Technology; Environment; Highways; Safety and Human Factors;
Filing Info
- Accession Number: 01993243
- Record Type: Research project
- Source Agency: Transportation Research Board
- Contract Numbers: Project 20-30, IDEA 263
- Files: TRB, RIP
- Created Date: Jun 23 2026 1:47PM