By Amber Liggett
It’s the most wonderful time of year for winter weather enthusiasts. If you’ve ever wondered how to forecast frozen precipitation or if you’re considering making your first forecast this season, this article is for you. Winter weather forecasting can be complex, but with the right foundational tools and resources, you’ll be ready to create an accurate forecast.
Before exploring those tools, let’s review the three basic ingredients needed for winter storm development.
- Cold air—Temperatures must be below freezing both aloft and at the surface to support snow, sleet, and freezing rain.
- Moisture—Sufficient water vapor is needed to form clouds and frozen precipitation. Moisture enters a region where air flows across bodies of water (e.g., large lakes, oceans).
- Lift—A mechanism that forces moist air upward, allowing clouds and frozen precipitation to form. Lift occurs when warm air rides up and over cold air or when winds push air upslope along a mountainside.
Keeping these fundamentals in mind, let’s explore the essential tools for winter weather forecasting.
Surface Observations
Always start a forecast by evaluating surface observations. They tell you current air temperature, dew point temperature, wind speed and direction, and cloud cover.
You can view the latest hourly U.S. surface data from NOAA/NWS, which includes station weather plots along with the North American surface analysis map that adds fronts and pressure systems. These observations provide a foundational understanding of the surface and lower atmospheric conditions before examining radar, satellite, or model guidance.
Doppler radar
Doppler radar is one of the most popular forecasting tools for identifying precipitation type and intensity.
The most widely known base Doppler radar product is base reflectivity. Snow and light rain usually appear smooth (uniform), while heavier rain appears more cellular (nonuniform). Reflectivity provides a first look at what might be falling.
Let’s pair this with additional products to get a clearer picture.
Dual-polarimetric (dual-pol) radar products are much more effective in identifying snow on Doppler radar. They transmit and receive energy in both horizontal and vertical directions, allowing forecasters to better track precipitation intensity, direction, and speed.
Use these basic dual-pol products to differentiate between rain, snow, and ice.
- Correlation coefficient (CC)—Highly correlated precipitation with high CC values of 0.98–0.99 represent uniform particles like snow, and appear dark red/purple. Lower CC values indicate varied particle shapes and sizes and appear yellow, orange, and red. This is common in mixed precipitation.
- Differential reflectivity (ZDR)—Low ZDR values of less than 0.5 represent uniform particles that are similarly sized and shaped like snow, and appear dark blue on radar.
- Specific differential phase (KDP)—Ice crystals and dry snow produce near-zero KDP values, because there is little or no liquid water present to cause the differential phase shift. KDP values for melting or wet snow are fairly low. While KDP is more useful for rain forecasting, it adds context to winter weather forecasts when paired with CC and ZDR.
Satellite
A complimentary tool to our ground-based radar is weather satellites. Satellite imagery provides a comprehensive picture of storm movement and cloud patterns.
Common satellite products used in winter weather forecasting include:
- This product is best used during daylight hours to determine storm structure and shape, since the imagery depends on sunlight to sense clouds.
- The color scale may be black-and-white or geocolor.
- Snow-covered ground appears white but has a different texture and remains stationary compared to moving clouds.
- Landmarks like rivers and lakes help distinguish snow from cloud cover if the bodies of water are not frozen.
- This product can be used any time of day, as the imagery depends on temperature rather than sunlight to sense clouds.
- There are different color scales to depict the temperature, but for this explanation, we’ll use a blue-to-red color scale, where blue indicates cooler temperatures and red indicates warmer temperatures.
- High, cold clouds that are often associated with heavy snowbands appear in blue, while low, warmer clouds appear as red.
- This product shows the amount of moisture in the upper atmosphere.
- The color scale may be black-and-white or color, but we’ll use black-and-white for this explanation.
- Bright white areas indicate high humidity where heavy precipitation is likely to develop, while dark regions represent dry air.
- This is useful for tracking current conditions and overall atmospheric set-up, though it does not confirm precipitation at the ground.
Weather Forecast Models
Weather forecast models combine data from surface observations, satellites, radar, and other sensors including automated surface observing systems to simulate the atmosphere and predict future conditions. They are essential for accurate forecasting.
Two major types of models that we will discuss are global and mesoscale models.
Global Models
These provide large-scale forecasts and are useful for tracking storm systems several days in advance.
Mesoscale Models
These focus on regional details and are useful for short-term winter weather prediction.
Many weather enthusiasts view these models through free platforms such as Tropical Tidbits (where all of these model links lead).
Climatology
Forecasters also consider climatology—or historical weather patterns for a given region and time of year—when creating a forecast. Climatology helps provide context for typical snowfall amounts, identify common storm tracks, and recognize unusual or anomalous patterns. When climatology doesn’t account for short-term fluctuations and is less effective at predicting extreme events, it serves as another tool in the winter weather forecasting toolbox.
As you can see, creating a winter weather forecast is both a science and a skill. Forecasters combine real-time surface observations with radar and satellite data to understand what’s happening in the atmosphere. They consult numerical weather models to project how conditions may evolve and use local climatology to provide context for what is typical or unusual for that region and time of year. By combining these tools, forecasters determine the most likely scenario for a winter weather event. While every forecast carries some level of uncertainty, using this toolbox provides a strong foundation for producing accurate winter weather predictions.
Now it’s your turn to create a winter weather forecast.
