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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Research in Progress (RIP)</title>
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      <title>A Decision Support System for Operational Forecasting of New Snow Natural Avalanches</title>
      <link>https://rip.trb.org/View/2717332</link>
      <description><![CDATA[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.]]></description>
      <pubDate>Tue, 23 Jun 2026 13:47:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2717332</guid>
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    <item>
      <title>Effects of Explosives on Avalanche Frequency and Magnitude</title>
      <link>https://rip.trb.org/View/2540004</link>
      <description><![CDATA[The application of explosives for avalanche control is a widely used avalanche risk mitigation method in North America. It is considered a short-term risk management method in that it acts on immediate avalanche hazard when required during the winter. However, the effects of such practices on seasonal avalanche frequency and magnitude are complex and involve multiple factors. The objective of this study is to challenge the long-held belief that regular pro-active explosives control performed consistently throughout the season leads to more frequent but smaller avalanches and ultimately reduces the frequency of avalanches reaching further into the avalanche path runout zone where transportation corridors are typically located. It will be based on a statistical analysis of avalanche occurrence records and extreme runout estimation from avalanche paths with sufficient records from before and after the implementation of a regular explosives control program. The results are expected to help inform long-term planning decisions pertaining to investments into explosives avalanche control infrastructure.]]></description>
      <pubDate>Fri, 18 Apr 2025 13:03:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2540004</guid>
    </item>
    <item>
      <title>Low-Cost Internet of Things Sensors to Support Avalanche Monitoring Programs</title>
      <link>https://rip.trb.org/View/2505726</link>
      <description><![CDATA[This project will develop a novel avalanche monitoring system to automatically provide remote temperature and snowpack movement information to assess roadside avalanche risk and monitor avalanche activities. Drones will deploy the monitoring units into hazardous avalanche release zones and, at the end of the season, retrieve them. Stage 1 work will focus on developing and validating the basic functions and performance of the selected hardware and material, conducting simulation and small-scale real-life experiments, and making any necessary adjustments to the fundamental factors of the designed system. Optimal temperature sensor, GNSS (location) sensor, accelerometer, antennas, motherboard, and batteries will be selected to ensure compatibility and functionality of all components of the system. Each component's operations will be activated and integrated into the motherboard to optimize weight, performance, and reliability. An avalanche monitoring unit shell will be designed. The shell design will accommodate a drone delivery/retrieval mechanism. A weatherproof robust base station will be designed and constructed for positioning near the avalanche slopes of interest. The base station will receive data from multiple avalanche monitoring units, compile this information into a data log, and facilitate remote access of this data via an Internet connection. Simulations will be carried out in laboratory and outdoor settings to test the system under varied conditions and the collected data will be analyzed to guide enhancement of system. In Stage 2, the system will be refined in terms of design, robustness, and effectiveness based Stage 1 work. A full-scale experiment to evaluate the performance of the revised system will be conducted at Snoqualmie Pass above Interstate-90 in the Cascade Mountains. The system may go through several improvement and refinement iterations before it is deemed complete and fully operational. After system improvements, comprehensive real-life testing will be carried out in the Cascade Mountains. The testing may include multiple "test-revise" cycles, allowing for further refinement of the system based on real-world feedback and performance metrics. The final report will provide all relevant data, methods, and conclusions along with guidance on how to use the developed system.]]></description>
      <pubDate>Mon, 03 Feb 2025 22:35:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2505726</guid>
    </item>
    <item>
      <title>Avalanche Risk and Forecasting using small, uncrewed Aircraft Systems</title>
      <link>https://rip.trb.org/View/2431161</link>
      <description><![CDATA[The study objectives include the development of a small, uncrewed aircraft system (sUAS)-based ground-penetrating radar (GPR) system to assess avalanche risk and forecasting. The difference between this research and R219.03 - UASnow is that the research team will take advantage of prior University of Southern California (USC) and U.S. Geological Survey (USGS) assets and investments. This particular research will advance the team's previous design to provide data that can be used to assess avalanche risk in real-time using a platform, which will provide a low-cost, rapid, and high-resolution tool for field assessments of snow properties that may be indicative of avalanche risk. The purpose is to provide a platform where real-time decisions can be made to implement corrective measures after an avalanche-prone area has been identified. 

The integration of a Radio Frequency System on a Chip (RFSoC) will allow for faster sUAS flights, radar processing, and communications to the ground control system. Other platforms such as lidar-based sUAS will be collocated to validate the SD GPR data and conventional ground-based snowpack observations. Flights and field data collection will be conducted at an avalanche risk area along the U.S. 550 Mountain Corridor and specifically in the area of Coal Bank or Molas Pass. sUAS and ground-based snowpack data (snow cores, snow pits, and snow probes) will be collected to validate the lidar and SD GPR returns; however, extreme care will be taken when entering avalanche prone areas. Lidar flights will be conducted during snow-on and snow-off periods to map bare-earth elevations and spatial variations in snow depths derived from difference maps. The study objectives include the following: Top of snow; Bare earth; Snow depth; Bare earth, aspect and slope and snow surface slope (lidar derived); Spatial distribution of snowpack properties; Snow grain type at the surface (assuming a nominal 1mm diameter for snow grains, achieving this objective can only be assessed after the radar returns are processed); Layering in the snowpack; Snow-density profile; and Snow-water equivalent (SWE).
]]></description>
      <pubDate>Mon, 16 Sep 2024 08:40:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431161</guid>
    </item>
    <item>
      <title>Smart Sensor for Snow Avalanche Monitoring</title>
      <link>https://rip.trb.org/View/2310054</link>
      <description><![CDATA[This research project aims at developing prototype low-cost avalanche sensor that can be deployed by unmanned aircraft (drones) to inaccessible slopes above 
Washington State Department of Transportation's (WSDOT’s) roadways to provide direct snowpack information to support the assessment of avalanche risk.

The sensor units will have the following characteristics: (1) provide remote temperature and snow movement information (using a Global Positioning System (GPS) and/or acceleration sensors) on the snowpack in support of assessing avalanche risk and avalanche activity; (2) low enough cost (less than $100) that occasional loss of a sensor is acceptable and so that multiple sensors can be used in an avalanche release zone of interest; (3) a light and rugged shell so the sensor can be dropped and recovered (Max weight 0.5 kilogram); (4) a shell shape so the sensor will remain in the snow below the drone after it is released and will not roll or bounce down a slope; (5) bright color with a label with return information to facilitate manual recovery after snowmelt; (6) long term battery power for operations over a winter (6 months); (7) able to communicate with signal receiving tower via Wi Fi network or a cellular connection; and (8) long range communication (> 300 meters).

The sensor will be designed to form a network of the sensors deployed in an avalanche release zone above a roadway for area-wide data collection and avalanche risk monitoring.]]></description>
      <pubDate>Thu, 14 Dec 2023 13:39:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2310054</guid>
    </item>
    <item>
      <title>Integrated Avalanche Detection Warning &amp; Snow Distribution Mapping</title>
      <link>https://rip.trb.org/View/2190089</link>
      <description><![CDATA[The proposed research plan consists of multiple research objectives that include: (1) identifying minimum specifications for the hardware and location of infrasound sensors to detect avalanche activity along the Thane Road corridor; (2) determining the ideal placement of infrasound sensors to detect avalanche activity in the starting zones of Snowslide Creek, Middle Path, and Cross Bay Creek; (3) collaborating with subject matter experts to develop and implement an avalanche detection/warning system that can be used to alert officials of natural avalanches; (4) determining the size and location of natural avalanches using infrasound sensors; (5) conducting snow depth, avalanche size and distribution mapping using UAS with LiDAR and photogrammetry; (6) determining best available payloads to collect snow distribution data; (7) capturing and documenting avalanche occurrence spatially using UAS platforms to capture information during inclement weather; (8) developing standard operating procedures that can be shared with agency partners to showcase how emerging technologies can aid in avalanche hazard forecasting and decision making.]]></description>
      <pubDate>Fri, 02 Jun 2023 20:17:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2190089</guid>
    </item>
    <item>
      <title>Transportation Avalanche Research Pool (TARP) 2.0</title>
      <link>https://rip.trb.org/View/1943996</link>
      <description><![CDATA[Avalanche forecasting and mitigation are critical operations for transportation agencies that manage highways and railways that are exposed to avalanches. These operations are an important part of winter operations in many western states and have large implications for public and workers safety. The current Transportation Avalanche Research Pooled (TARP) Fund project (TPF – 5(337)) is ending, and the next phase of pooled fund work will begin under this new solicitation. This new TARP project will maintain the original mission of supporting collaborative research efforts toward avalanche hazard assessment and mitigation within the transportation industry. The goals are to improving public and worker safety, improve operational efficiency, improve and develop informational analysis tools, and explore beneficial avalanche technologies and techniques. Currently, almost all Transportation agencies that operate avalanche programs in North America are participating in TARP and have expressed great interest in participating in the next phase of the project.]]></description>
      <pubDate>Mon, 25 Apr 2022 19:24:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/1943996</guid>
    </item>
    <item>
      <title>Transportation Avalanche Research Pool (TARP)</title>
      <link>https://rip.trb.org/View/1375556</link>
      <description><![CDATA[The study's mission is to support collaborative research efforts in the field of avalanche hazard assessment and mitigation, with the goal of improving the safety, efficiency, and quality of control efforts, along with providing better information gathering and analysis techniques and seamless integration of new technologies to further these goals. The participation of many transportation related agencies in this study will also further cooperation in this industry, leading to improved future development of beneficial technologies and improved sharing of information and avalanche data, greatly furthering the safety, efficiency, and quality of the work done in this field for all relevant agencies.]]></description>
      <pubDate>Mon, 23 Nov 2015 13:02:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/1375556</guid>
    </item>
    <item>
      <title>Field Operations for GPS assisted Winter Maintenance Vehicles</title>
      <link>https://rip.trb.org/View/1234346</link>
      <description><![CDATA[The objective of this task was revised based on interviews and discussions with the customers in December of 2010. The new description is as follows. Research and development for avalanche sensing, prediction, and detection. Evaluate related state-of-the-art and commercial systems. For promising commercial systems, provide guidance and assistance to Caltrans maintenance for site and operation-specific deployment. Sensing regimes to be investigated are expected to include snow depth across avalanche start zone, water content across avalanche start zone, snow movement in start zones. Additional sensing regimes and technologies will be investigated based on results from our review. Identify most promising existing prediction, sensing, and detection models; update / enhance these models if warranted. Instrument one key District 10 avalanche zone (e.g. "The Spur"), collect data for one snow season. Note that procurement of system(s) will be limited by currently available resources. Correlate data with prediction, sensing, and detection models.]]></description>
      <pubDate>Thu, 03 Jan 2013 15:10:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/1234346</guid>
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