Predicting snow above freezing

November 2022 - Led by Keith Jennings and Nayoung Hur
Written by Sonia Nieminen

Using your observations to predict the probability of snow and rain.

The first step toward improving our ability to predict snow versus rain was to calculate the probability of snowfall. By estimating the probability of snowfall for each region, we can make better predictions for the future, in the same way that you might predict that you have a 1 in 2 chance of tossing heads on a coin. 

To do this, we paired each observation of rain, snow, or mixed precipitation with known local temperature data. We then created a graph with the temperature on the x-axis, and the number of observations at each temperature on the y-axis (light green dots on the figure called the "Anatomy of a snowfall probability curve"). Just like the number of tosses of a coin, the number of observations of snow at each temperature allows us to calculate the probability of snowfall at that temperature in each region. For example, in a certain location, if more people report snow at 4°C (39.2°F) than they report rain, then over time, we can conclude that the probability of snow at 4°C (39.2°F) is higher than the probability of rain.  

The last step in creating snowfall probability curves is to fit a smooth line through the data points, which is essentially a simple model. On the right, you can see the black line is "fitted" between the light green dots to create the curve.

If you'd like a refresher on why our team is studying the rain-snow transition, check out our posts on the EGU Blog or on SciStarter.

"Anatomy" of a snowfall probability curve.

Description of a snowfall probability curve.

Below are the snowfall probability curves for some of the Mountain Rain or Snow regions. See if you can notice the difference in the probability of snow at 0°C (32⁰F) for each of the regions. The reason these curves are different is because the meterological processes driving the rain-snow transition differ by region. 

 Sierra Nevada snowfall probability curve.
Colorado Plateaus snowfall probability curve.
Northeastern Highlands snowfall probability curve.
 Eastern Great Lakes Lowlands snowfall probability curve.

Comparison of snowfall probability curves.

Comparison of snowfall probability curves across various level 3 ecoregions.

Now look at the comparison of snowfall probability curves. Follow the dotted line at 50% probability of snowfall: you can see that the temperatures at which rain is likely to transition to snow are not the same for all regions.  The average temperature at which rain transitions to snow in each region is unique. Here is a snapshot of the rain-snow transition in some of your regions: 4.6°C (40.3°F) in the Rockies; 2.1°C (35.8°F) in the Sierra Nevada; and 0.7°C (33.3°F) in the Northeast US. 

This means that a model or algorithm using a threshold of 0⁰C (32⁰F) for all regions may not likely be accurate when making predictions of rain vs. snow near the freezing point. 

The key takeaway here is that the temperatures at which rain transitions to snow vary by region, and we need to take this variability into account in future predictions. 

Using probability of snowfall and rainfall to create a "report card" on the success rate of satellite technology.

Now that we have calculated the probability of snowfall with community observer data, we can compare those predictions to those generated by satellite technologies. Specifically, we assessed the prediction accuracy of the Global Precipitation Measurement (GPM) mission, one of NASA’s missions, which uses an algorithm that is abbreviated as “IMERG” to predict rainfall/snowfall. We can compare the probability of snowfall or rainfall generated by Mountain Rain or Snow observers with that of IMERG, region-by-region. 

The probability of liquid precipitation from NASA GPM was compared to the frequencies of precipitation phase reported by community observers. This helped us see the IMERG success rate at different temperatures in different regions. 

Community observations can help us to pinpoint where the IMERG algorithm struggles to predict the correct precipitation phase. When comparing the ground-based observations from community observers with estimates from NASA, we can see some areas in which IMERG struggles. In the figure on success rates, the darker orange colors indicate lower success rates, and the lighter orange colors indicate higher success (white indicates "no data"). 

What’s interesting is that the success rate of IMERG is not consistent across regions: see how it predicts too much rain in the Sierra Nevada region just above 0⁰C (32⁰F), and it predicted too much snow for the Southern Rockies at warmer temperatures. 

This is a big step towards improving the technologies that predict the rain-snow transition.

Success rates of IMERG satellites.

IMERG success rate across various level 3 ecoregions.

Regional differences: Why they matter.

Recognizing regional differences gives us the traction we need to determine what other meteorological processes need to be included in models that predict rain-snow thresholds.  Our scientists will continue work behind the scenes using the latest machine learning and modeling methods to advance algorithms to predict the rain-snow line. Your continued participation will help keep the project advancing forward.  

Observations from geographically and climatically diverse areas are so important to this project. Your observations from high in the mountains, all the way to the low-lying valleys, are valuable to make locally-relevant predictions and improve the technology behind rain-snow estimates. 

We appreciate your continued involvement in the project and it would not be possible to make these scientific advances without you. 

Mountain Rain or Snow logo with graphics of mountains, a snow crystal, and a rain drop.