December 2025 - Research by Bjoern Bingham & Anne Heggli, written by Luisa Ortega
The “rain-snow line” is the elevation where rain begins to fall as snow, and it can be measured both on Earth’s surface and in the atmosphere. If you have ever driven over a mountain pass and noticed rain change to snow on your way to the top, you have experienced the “rain-snow line”. Understanding precipitation phase is important, but most current weather tools can't reliably tell whether precipitation is rain or snow.
The elevation of rain-snow line provides scientists and weather forecasters with information about how precipitation changes during a storm. Like how we often check our weather apps to see how to dress for the day, scientists and meteorologists rely on information about the rain-snow line to make informed decisions about avalanche safety, transportation, and water management – but it can be very difficult to forecast and even detect in real-time.
To identify rain–snow lines in the atmosphere, the MRR measures the bright band height (BBH), which identifies the zone in the sky where snow melts into rain; this zone is referred to as the “melting height.” To determine the BBH, radar signals are sent upward toward the sky, where they interact with raindrops or snowflakes. The way the radar signal bounces off precipitation reveals the altitude where snow turns to rain: snow reflects less of the signal, while rain reflects more. Thus, a sudden increase in radar reflectivity indicates the melting height. In addition to reflectivity, the MRR uses falling velocity to help determine the BBH. They can measure the fall speed of precipitation, which provides further information about whether particles are snow or rain which helps estimate droplet size.
Though MRR stations are used daily to help meteorologists predict the weather, relying on these datasets has its limitations. For example, radar measurements can only provide a range of estimated values, the data is often inferred to capture surrounding regions, or it can simply go offline due to maintenance or technical issues.
In this study, BBH data from the MRR station near Colfax, CA is used in tandem with Mountain Rain or Snow observations from February to December 2024 along the I-80 corridor over the Sierra Nevada Mountain pass between Colfax and Reno, NV to understand the variation of the snowline across elevation and time.
Mountain Rain or Snow observations are especially useful for pinpointing rain-snow lines because they give accurate, ground-level reports that remote stations often miss. Since MRR stations are few and far between, Mountain Rain or Snow helps fill in important gaps across the landscape. Unlike MRR stations, which are sparsely distributed and offer limited coverage across the landscape, Mountain Rain or Snow observations are more widespread and can fill in important observational gaps. Using both datasets together lets us combine the detailed radar data from MRRs with real-time experiences from Mountain Rain or Snow, giving a more complete picture of the rain-snow transition.
Figure 1. Project study area between Colfax, CA and Reno, NV along the I-80 corridor (Domain). The study area is divided into three subsections (Windward, Crest, Leeside) to capture the variability of elevation, temperatures, and wet or dry climate along the stretch of highway. Additionally, the study area sees a high number of Mountain Rain or Snow observations, allowing for sufficient data to be compared between the two data sets. The Colfax radar station (MRR station) is denoted as the red triangle.
To assess the rain-snow line along the I-80 corridor, Mountain Rain or Snow team members Bjoern Bingham and Anne Heggli at DRI compared the elevation of the Colfax radar data to Mountain Rain or Snow observations.
Four case studies were conducted for four winter storms in 2024 to find patterns in precipitation along the study sites (see figures below*). To recap, an increase in radar reflection suggests that precipitation type is rain, so the radar elevation data should closely match the elevations of observed rain (green) or mixed precipitation (pink). If there are gaps in the MRR data, this means that there was a period of no precipitation, or the melting height is below the station itself.
*Each plot shows Mountain Rain or Snow observations in green for rain, pink for mixed, and blue for snow. The Colfax MRR station data is shown in black bold X's. In general, the Colfax data should follow the trends of the green and pink observations to be considered a ‘good’ match between datasets.
Case Study 1: February 3-5, 2024
During the February storm, the MRR data and Mountain Rain or Snow data show that there is a mismatch between observations and radar. Cold pooling in the Reno valley likely contributed to this, as trapped cold air did not allow the precipitation to transition to rain and it continued to snow. This case also illustrates how MRR stations, which only observe vertically, can miss important geographic variations in terrain – misinterpreting what people are experiencing in real-time.
Case Study 2: February 29-March 3, 2024
In Case Study 2, the MRR data and Mountain Rain or Snow observations lined up well, making it easier to track where the rain-snow line was on the mountain. The data showed that the snow line dropped to lower elevations as the storm went on. This case also shows how the two datasets can back each other up—when one has gaps, the other helps fill them in.
Case Study 3: November 22-24, 2024
Like Case Study 2, the late fall storm provided valuable data from both the Colfax data and Mountain Rain or Snow observations. While both datasets generally agree that the snow line dropped over time, the radar initially showed it at a higher elevation than what observers reported. These differences are useful for analysis, offering insights into data quality, helping scientists make better decisions, and pointing to areas where observations can be improved.
Case Study 4: December 13-15, 2024
The fourth storm highlights the scenario where the MR didn’t detect any precipitation and couldn’t collect data, but Mountain Rain or Snow observers continued to report conditions on the ground. The observations helped fill critical gaps, showing the value of having both datasets working together. This case also underscores how local, ground-based reports can offer detailed spatial coverage that’s especially useful for forecasters, emergency responders, and water managers making real-time decisions.
To better understand how well one MRR station reflects a bigger area, the researchers made a heatmap. They compared the MRR data with Mountain Rain or Snow observations by distance from the station and by year (Figure 6). Heatmaps are helpful because they use colors to show the data, making it easy to spot where the match is good or poor. Results of the analysis show that distances under 50 km (about 31 miles) have the best agreement (70.0 - 88.7%).
However, Mountain Rain or Snow observations farther from the MRR (50–100 km) don’t match as well. This is because the MRR only measures conditions directly above it, while storms change as they move up the mountains. The rain–snow line can shift with elevation and terrain, so what the MRR sees in Colfax may not reflect conditions at the crest or on the other side of the mountain. The heatmap emphasizes the differences in rain-snow lines that are expected in mountainous regions, suggesting a need for more locally observed rain and snow observations to understand the differences.
Figure 6. Distance heatmap analysis between Colfax radar data and Mountain Rain or Snow observations as a percentage (%) and number of observations (in parentheses). Observations from 2020-2024 are compared to distances from the Colfax station according to the three study site regions – windward (under 50 km), crest (50-100 km), and leeside (all distances). Dark blue means there is a strong match and yellow means there is a weak match.
With the analysis of this study, we learned that two datasets are better than one. When two datasets work together, we can get a more complete picture of actively falling precipitation. Furthermore, building a long-term data library – like Mountain Rain or Snow observations – can help understand the differences in weather patterns and phase change. It can also help scientists improve instrumentation accuracy and address the limitations of radar stations like the MRR. Importantly, Mountain Rain or Snow data collected by a large network of observers can be shared in real time with weather forecasters, water managers, and emergency responders. This means the data is not only valuable for long-term research, but also immediately useful for making decisions during winter storms.
The Mountain Rain or Snow data provides reliable, real-time information that helps keep people safe and supports smart water resource management. While the MRR offers a continuous snapshot of the atmosphere above one location, it can’t capture how conditions change across the mountains. That’s where the mountain Rain or Snow community shines—validating the MRR or showing when the atmosphere has shifted farther away. Together, the MRR and Mountain Rain or Snow tell a fuller story of rain and snow, giving forecasters and managers the tools they need to act quickly and plan ahead. This combined approach helps communities respond more effectively to changing weather—whether at work, at home, or while enjoying the mountains.
Bingham, B., Hatchett, B., Hegglie, A., Collins, M., Tonino, S., Hur, N., Jennings, K., Rienzo, M., & Mountain Rain or Snow Observers. (2025). Complimentary datasets: Mountain rain or snow and vertically pointed snow level radar
