RESEARCH EXPERIENCES FOR UNDERGRADUATES (REU)

Seeded and Natural Orographic Winter Storms and Catchment Processes Evaluation (SNOWSCAPE) Project

Alyssa Stansfield, Assistant Professor

Scientific Background

This winter, Utah’s northern Wasatch Range will become a natural laboratory for SNOWSCAPE, a collaborative field campaign with a goal to investigate winter-storm processes and the impacts of ground-based cloud seeding on snowfall, snowpack accumulation, snowmelt, and runoff. The northern Wasatch Mountains were chosen because of frequent and abundant winter storms and cloud systems, the existence of nearby ground-based seeding systems, the ability to position and access instruments at upper elevations at local ski resorts, proximity to the Great Salt Lake and Wasatch Front urban area, importance of the Weber and Ogden Rivers for regional water resources and recharge of the Great Salt Lake, and ease of accessibility for scientists from nearby institutions.  This project will advance our understanding of the efficiency of ground-based cloud seeding in a variety of winter storm conditions and of complex microphysics governing the formation of frozen precipitation in mountain environments.

 

Student's Role

The student will analyze data from a variety of instruments deployed during SNOWSCAPE, including radars, radiosonde balloon soundings, ceilometers, precipitation gauges, and radiometers. Based on their interest, the student can either perform an in-depth case study of one storm or compare characteristics across different observed winter storms.

Student Learning Outcomes and Benefits

• Work with real data collected during winter 2026 in northern Utah.

• Learn about different instruments used to measure atmospheric data and what variables they measure.

• Study the diversity of winter storms that hit Salt Lake City.

• Learn about cloud microphysics and how precipitation forms in mountain environments. 

• Understand the science behind cloud seeding.

Monitoring Personal Exposure to Heat and Air Pollution using Wearable Sensors

Alexandra Ponette-González, Associate Professor

Scientific Background

Outdoor heat and particulate matter (PM) air pollution are growing threats to human health in cities across the American West, and they are even more harmful when they occur simultaneously. To protect people, communities need to know what individuals are feeling and breathing on the ground and as conditions change from hour to hour.   Accurately characterizing personal exposures to heat and PM is key to developing effective exposure mitigation and adaptation strategies. Building on a pilot summer 2025 field campaign, we will monitor personal exposure to summer heat and air pollution in Salt Lake City and additionally conduct direct physiological measurements to document how people respond to real-time urban environmental stressors. Specifically, we will:   (1)   Collect data on summer ambient temperature, relative humidity, pressure, physiological stress, and microenvironment characteristics along designated routes using multiple sensors.  (2) Clean, organize, and analyze sensor data and visualize exposures across the study area. (3)   Begin to formulate exposure mitigation planning and management strategies.

Student's Role

In this project, we will use low-cost heat and PM sensors to assess personal exposure to urban heat and air pollution in Salt Lake City and physiological monitors to measure human-scale responses. The REALM REU student will work with co-mentors Ponette-González and Daher and a small team of researchers monitoring personal exposures in Salt Lake City. The student will participate in field data collection by wearing low-cost sensors and walking along designated routes in Salt Lake City. The student will organize, conduct preliminary analysis of, and visualize sensor data. Using these data, the student will contribute to exposure mitigation and adaptation planning recommendations.

Student Learning Outcomes and Benefits

With support from the research team, the REALM REU student will be involved in the entire process of this cutting-edge personal exposure research: from data collection to analysis to presentation. By the end of the project, the student will:

• Gain knowledge and understanding of intraurban variability in heat and air pollution and personal exposure monitoring. 
• Obtain hands-on research experience in field data collection using low-cost wearable sensors as well as physiological monitors.
• Develop skills to organize and analyze spatial data using GIS, Excel, JMP or R.
• Work as part of a collaborative team of students and faculty.
• Communicate scientific findings to peers during group meetings as well as in a scientific setting.
• Produce high-quality figures for presentations.
• Refine scientific communication and presentation skills, including at the Undergraduate Research Symposium..

Quantifying and parameterizing mixed-phase microphysical properties using novel disdrometer

Erik Pardyjak, Professor and Tim Garrett, Professor

Scientific Background

A changing climate is expected to induce a transition from snow to mixed-phase precipitation and rain. High-elevation snowpacks provide a natural reservoir that stores water for drinking, agriculture, forests, and aquatic ecosystems and releases it in the spring as the snowpack melts. These natural reservoirs, and hence the hydrologic cycle, are susceptible to the increase in variability of precipitation amount and type. However, it is difficult to accurately measure the rain-snow petitioning of hydrometeors. Our team has developed and patented an instrument called the Differential Emissivity Imaging Disdrometer (DEID), which has been used to thermally image individual hydrometeors (i.e. snowflakes and rain droplets) to determine their mass and other microphysical properties. We are attempting to extend the technique so that mixtures of frozen and liquid water can be quantified.

Student’s Role

The student will work with the DEID and its datasets by conducting experiments in the laboratory and cold room to understand its calibration. The student will also have the opportunity to analyze datasets from the previous season's field measurements.

Student Learning Outcomes and Benefits

The student will learn more about the microphysics associated mixed-phase precipitation including mass and size distributions. The student will obtain hands-on experience with a custom meteorological instrument. The student will understand laboratory procedures and data analysis including Matlab/Python data processing.

Computational tools for wildfire smoke detection in the western U.S.

Heather Holmes, Associate Professor

Scientific Background

Over 70 million people live in the western U.S., 24% of the total U.S. population. Thepopulation density in this region is low, and correspondingly should have fewer sources ofanthropogenic air pollution. However, areas in the western U.S. have acute, elevated levelsof air pollution concentrations, exacerbated by unique air pollution sources like wildfiresmoke. The number and size of wildfires are increasing due to climate change and severedrought. Wildfire smoke has public health consequences for populations downwind,including altering behaviors (e.g., individuals forgoing outdoor activities) and smokeinducedillnesses. Wildfire smoke often leads to cities having air pollution concentrationsthat exceed the national ambient air quality standard. Knowing which air pollution eventsare attributed to smoke plumes is helpful for regulatory agencies and health effectsresearchers. This goal of this project is to develop a tool to automate the detection ofwildfire smoke contributions to poor air quality.

Student's Role

The student will help develop a scientifically robust method to identify wildfire smoke daysin an urban environment. Over the course of this research experience, the student willdevelop a piece of the computational tool that several other researchers and students arecontributing to. The student will use open-access geoscience datasets (i.e., EPA air qualitydata, meteorological observations, and satellite fire detections) as the foundation of thedetection method. Then the student will incorporate atmospheric models and statisticalapproaches with the datasets to develop a smoke detection algorithm. The result will be acomputational tool written in R for air quality agencies and researchers to use for policyand health assessments.

Student Learning Outcomes and Benefits

At the end of this project, the student will be able to:

• Explain the basic physics and chemistry of wildfire smoke plume transport.
• Recognize reputable open source data (e.g., best file format, metadata).
• Implement R codes to read and write large datasets.
• Generate R codes to build statistical models using these large datasets.
• Produce descriptive, graphical visualizations to share findings and communicate scientific meaning.

Physics of Wild Fires

Derek Mallia, Research Assistant Professor and Steve Krueger,  Professor

Scientific Background

Wildfires are increasing in both frequency and size, leading to greater destruction of structures, loss of life, and deterioration of air quality. Larger and more intense wildfires could also be increasing the frequency of fire-initiated thunderstorms, i.e., pyrocumulonimbus (pyroCb), which can result in erratic fire weather conditions around the wildfire. Population growth in the wildland-urban interface (WUI) without adequate consideration of wildfire risks has also contributed to wildfire losses. Inadequate maintenance of electrical power lines has also sparked several disastrous wildfires in California. Wildfire intensity and spread is determined by three factors: fuel, wind, and slope. Fine fuels respond to dry conditions most quickly and are easiest to ignite. Fuels and winds vary greatly in short distances in mountainous terrain. Fires spread more rapidly upslope than downslope. Coupled fire-atmosphere numerical weather prediction (NWP) models are increasingly being used to guide real-time wildfire decisions.

 

Student's Role

Our research primarily involves improving fire weather and behavior forecasts. While fire-atmosphere NWP models are considered the gold standard for forecasting wildfire behavior, these models are computationally intensive to run and cannot be applied to large number of wildfires burning simultaneously like what we’ve observed in previous wildfires seasons, i.e, the summer of 2020. For this project, the student will help run a full physics NWP model that can account for fire-atmosphere interactions such as pyroCbs. The student will then use the fire-atmosphere NWP model output as a training base for machine learning and artificial intelligence approaches to develop a new parameterization that can forecast future fire activity and fire-atmosphere interactions. Output from this parameterization will then be compared to output from existing wildfire forecasting systems and one-dimensional plume rise simulations generated from a simplified physics-based parameterization.  The student will analyze the results fire-atmosphere NWP models with the goal of using the results to (1) better understand wildfire behavior under various fuel and weather conditions, and (2) determine the predictability of future fire activity.

Student Learning Outcomes and Benefits

At the completion of this research experience, the student will:

• Gain experience working with NWP that can resolve fire-atmosphere interactions
• Have met and talked to scientists who are developing and testing state-of-the-art coupled fire-atmosphere numerical weather prediction models.
• Learned about the basic physical components of fire spread and fire plume rise and how they are represented in numerical models.
• Be exposed to how fuel moisture is measured and modeled.
• Learned about the different modes of fire spread and various aspects of extreme fire behavior.
• Develop machine learning and artificial intelligence methods to forecast wildfires using NWP model output as a training database.

Field Work in Basin, Urban, and Mountainous Terrain

John Horel, Professor, and Colin Johnson, Research Associate

Scientific Background

Our group maintains a network of stations from remote locations on the Great Salt Lake, to urban sites in the Salt Lake Valley, and up through Red Butte Canyon Natural Resource Area into the Wasatch Mountains. We also may be deploying additional sensors in Red Butte Canyon during summer 2025 to help support the Salt Lake City Summer Ozone Study (SLC-SOS).  Our group is involved in research and development related to data science, machine learning, fire weather, the Great Salt Lake, flash floods, and air quality along the Wasatch Front (see https://horel.chpc.utah.edu). The REALM student working with us will be involved in a mix of hands-on field work helping to maintain our network of weather stations as well as learning methods to access, archive, quality control, and disseminate observations from a wide variety of sensors.

 Student’s Role

You will participate in research with faculty, staff, and graduate and undergraduate students in the Horel group (https://horel.chpc.utah.edu). You will have the opportunity to spend time assisting with fieldwork related to environmental monitoring stations operated by our group. You will obtain hands-on experience with field safety procedures and how weather equipment is installed, maintained, and operated. Background in the atmospheric sciences is not required nor is prior field work or any specialized field training. You should be willing to occasionally hike for a mile or more, often beginning early in the morning to avoid missing out on other REALM-scheduled activities (and avoid heat and bugs!).

Student Learning Outcomes and Benefits

• Obtain hands-on experience involving environmental sensors that requires following common safety procedures.
• Become familiar with software used to access data from dataloggers while in the field and remotely.
• Become familiar with the linux operating system on workstations housed by the Center for High Performance Computing.
• Be involved in societally-relevant research contributing to protecting lives and property from automated weather reports monitoring hazardous weather and improving public awareness of unhealthy air quality.

Air Quality Study in Utah

Gannet Hallar, Professor

Scientific Background:

In the Western US, many continue to face poor air quality in the summer due to both high ozone concentrations and increased aerosol loading due to wildfire smoke.  Increased ozone and aerosol concentrations have been linked to increased instances of respiratory and cardiovascular hospitalizations. As the Western U.S. experiences rapid population growth and increases in fire activity, exposure of Americans to adverse air quality from wildfire smoke will become an increasing concern, and duration of exposure and resulting impact on the health system. Increase wildfires should be viewed as a merging public health threat.

Student’s Role

The REU student will work closely with the Hallar Aerosol Research Team (HART) to investigate aerosol loading associated with wildfire smoke in Utah.   There are two objectives for this summer project:   1. Participation in collecting data via the Salt Lake City Summer Ozone Study.  2. Working with a team to link environmental data and health data.

Student Learning Outcomes and Benefits

The student will be engaged in the Salt Lake City Summer Ozone Study (SLC-SOS) with a focus on preparing air quality forecast for logistical field planning.  The SLC-SOS is a collaboration between University of Wyoming, Utah Division of Air Quality, the University of Utah and Colorado State University (along with many others). The student’s learning outcome will focus on gaining experience in airborne research (U. of Wyoming King Air) to study air quality in an intermountain basin. This campaign will enable undergraduate students to examine profiles of wind, temperature, aerosol properties, and trace gases in the boundary layer in the Salt Lake Valley.  The overall goals of SLC-SOS are to use airborne measurements to investigate ozone production along the Wasatch Front between July and August 2026. Next, the student will be engaged with a team focused on understanding the health impacts due to wildfire smoke exposure on days that are classified as exceptional events by the Environmental Protection Agency.  Here the REU student will use and improve data analysis skills (e.g. R or Python), while involved in societally relevant research.