How Scientists Predict Winter Snowfall in the United States

A Pacific storm route crosses a U.S. silhouette, with snow and rain showing how temperature shapes winter precipitation.

Will your winter be snowy—or will the storms mostly bring rain? Scientists can offer useful clues months ahead, but predicting snow is not one task. It is two: estimating the kind of winter a region might have, and forecasting what an individual storm will do.

The distinction matters because snowfall depends on more than moisture. A storm must take the right path, encounter the right temperatures, and produce snow that accumulates on the ground. Here is how forecasters put those pieces together—and why their answers become more specific as a storm approaches.

Two Forecasts, Two Different Questions

A seasonal outlook describes the odds of broad conditions over several months. A storm forecast estimates the weather from a particular approaching system.

Forecast	Main question	What scientists examine	What you can reasonably expect
Seasonal outlook, months ahead	Will winter favor warmer, colder, wetter, or drier conditions?	Pacific Ocean patterns, recent climate trends, and climate models	Regional probabilities—not a dependable snowfall total for your town
Storm forecast, days ahead	Where will snow fall, and how much might accumulate?	Storm track, moisture, temperatures at different heights, and weather models	Increasingly local estimates, with a range of possible outcomes
Updates during a storm	What is happening now, and what comes next?	Radar, surface reports, and other observations	Revised estimates as snow bands and precipitation types become clearer

The most important distinction is precipitation versus snowfall. Precipitation includes rain, snow, and other forms of water falling from the atmosphere. A wet winter is not necessarily a snowy winter: much of that water may arrive as rain.

That is why NOAA’s seasonal outlooks emphasize probabilities for temperature and total precipitation rather than precise city-by-city snowfall totals.

Months ahead, scientists forecast the odds of favorable conditions. Days ahead, they forecast the storms that can actually produce snow.

How the Pacific Ocean Influences a U.S. Winter

One of the strongest seasonal clues comes from El Niño–Southern Oscillation, usually shortened to ENSO. This is a linked pattern of tropical Pacific Ocean temperatures and atmospheric circulation.

Its two better-known phases are:

El Niño: unusually warm surface water in the central and eastern equatorial Pacific.
La Niña: unusually cool surface water in that region.

These distant changes can influence U.S. winters by altering the jet stream—a band of fast-moving winds high in the atmosphere that helps guide storms and marks boundaries between warmer and colder air.

Ocean temperatures do not push storms directly

Think of the Pacific as changing the atmosphere’s heating pattern rather than acting like a conveyor belt for storms.

The process has three broad steps:

Tropical rainfall shifts. During El Niño, warmer waters favor rising, rain-producing air farther east across the tropical Pacific. During La Niña, that activity is concentrated farther west.
Atmospheric circulation responds. The changed pattern of heating reshapes winds and generates broad atmospheric waves that can extend toward North America.
Storm tracks shift. Changes in the Pacific jet stream alter the routes that winter storm systems tend to follow.

NOAA’s explanation of ENSO and the winter jet stream describes these as recurring tendencies—not fixed paths that every storm must follow.

What those shifts can mean for snow

ENSO phase	Typical winter tendency	Possible snowfall implications
El Niño	A more active southern U.S. storm track	Historically, more snow in some colder southern areas and higher elevations of the West, but less across much of the northern U.S.
La Niña	A storm track favoring the Pacific Northwest and northern U.S.; a generally warmer, drier South	Cooler, wetter conditions can favor snow in parts of the Northwest, while southern conditions are often less favorable

These patterns vary by location and from one ENSO event to another. Other atmospheric patterns can redirect storms, and even a stormier region needs sufficiently cold temperatures to receive snow.

El Niño or La Niña therefore cannot tell you how many snowstorms your town will get—or how many inches they will deliver.

Why Spring Forecasts Are Especially Difficult

Pacific signals are often most useful for winter outlooks by late summer and fall, when scientists have a clearer picture of whether El Niño or La Niña will develop or persist.

Forecasts made earlier must contend with the spring predictability barrier: a recurring drop in ENSO forecast skill for predictions made during, or extending through, Northern Hemisphere spring.

A transition period with a weak signal

Spring is often when an existing ENSO event fades, while the next pattern has not fully emerged. The developing ocean–atmosphere signal may be weak compared with short-term wind fluctuations.

For example, a burst of winds blowing from west to east can help a developing El Niño grow. But forecasters cannot always know whether those bursts will recur or persist.

The Pacific’s seasonal cycle also changes how disturbances evolve. Small differences in the starting conditions or in a model’s behavior can become especially consequential for forecasts crossing spring. Scientists study several interacting mechanisms; there is no single agreed-upon cause of the barrier.

Better observations help—but do not remove the limit

Moored buoys, anchored instruments in the ocean, and Argo floats, drifting instruments that measure conditions through the water column, reveal temperatures below the surface. Subsurface changes can provide clues before the full ENSO pattern appears at the surface.

These observations improve the starting conditions for coupled ocean–atmosphere models, which simulate how winds and ocean temperatures affect one another.

Researchers also test information from other ocean basins and machine-learning methods. Some approaches show improvements in retrospective tests—forecasts for past years made using only information that would have been available at the time. That is encouraging, but it does not guarantee equally strong performance in routine forecasting.

As NOAA explains, better observations and models reduce uncertainty without eliminating it. And a better ENSO forecast still does not become a precise U.S. snowfall forecast.

Turning an Approaching Storm into a Snowfall Estimate

Once an actual storm begins to develop, forecasters can tackle questions that seasonal outlooks cannot answer.

1. Where will the storm go?

The storm track determines where moisture travels and where heavy precipitation develops. A modest shift can move a narrow band of heavy snow away from your town.

This is especially important near a sharp snowfall boundary: neighboring communities can receive very different totals.

2. Will the precipitation stay snow?

Forecasters examine the vertical temperature profile—the temperatures at different heights between the clouds and the ground.

The temperature at your doorstep is not enough:

Snow that remains frozen on its descent can reach the ground as snow.
Snow that melts in a warm layer and refreezes before landing can become sleet, or ice pellets.
Melted precipitation that remains liquid until it contacts a freezing surface can become freezing rain.
If conditions are warm enough through the lower atmosphere, it arrives as rain.

A small change in a warm layer can therefore sharply reduce the predicted snowfall.

3. How much snow will the available water produce?

The snow-to-liquid ratio describes how deep fresh snow is compared with the depth of water it would produce if melted.

At a ratio of 10:1:

0.1 inch of liquid water \times 10 = 1 inch of snow .

In words, one-tenth of an inch of water makes one inch of fresh snow at that particular ratio.

But 10:1 is not a universal rule. Snowflakes and their packing differ with atmospheric conditions, so the same amount of water can produce different snow depths. Forecasters must estimate both how much precipitation falls as snow and how much depth that snow produces.

Why Snowfall Models Disagree

A weather model uses observations and equations describing atmospheric behavior to calculate how weather may evolve. Because neither the observations nor the calculations are perfect, one model run cannot capture every plausible future.

An ensemble addresses this by running a forecast many times with slightly different plausible starting conditions. Some ensembles also vary how the model represents physical processes it cannot calculate in full detail.

Those small differences grow over time, producing different storm tracks, temperatures, and precipitation amounts.

Snow combines several uncertainties

Snowfall disagreements often come from three interacting ingredients:

Track: Does the heavy-snow band cross your town or pass nearby?
Precipitation type: Does a warm layer turn some snow into sleet or rain?
Snow-to-liquid ratio: How much depth does the snowfall’s water content produce?

There is also a difference between members of one ensemble and separate forecast models. Ensemble members deliberately explore uncertainty. Separate models can disagree because they use different methods and assumptions.

And agreement is not proof: models can share errors.

Why probabilities can be better than averages

Suppose some forecasts show a storm missing your town, while others show heavy snow. An ensemble average might suggest a moderate total—even though that middle outcome has little support.

A statement such as “a 60% chance of at least 4 inches” can communicate the forecast more usefully. Forecasters consider how many members support an outcome, their spread, and known model limitations rather than simply counting snowfall maps. The Weather Prediction Center’s probabilistic winter guidance is designed to express this uncertainty.

How Scientists Measure What Actually Fell

Forecasts and satellite estimates need observations to check them. But “how much snow” can mean several different things.

New snowfall, snow depth, and water content

New snowfall is the fresh accumulation during a specified observation period. Trained observers commonly measure it with a ruler on a flat snow board, then clear the board for the next period.
Snow depth is all the snow remaining on the ground, including older snow.
Liquid-water equivalent is the depth of water represented by the snow if melted.

These quantities are not interchangeable. Ten inches of fresh snowfall may leave less than ten inches on the ground after settling, melting, or wind redistribution.

Automated mountain stations in the SNOTEL network monitor snowpack—the accumulated snow on the ground. A snow pillow measures the weight of snow above it to estimate the water stored in that snowpack.

Satellites see different pieces of the picture

Precipitation satellites use radar and microwave measurements to estimate snow falling through the atmosphere. Converting signals from ice particles into snowfall rates is difficult because snowflakes vary in shape, size, and density.

Snow-cover satellites map where snow lies on the ground. A snow-cover map does not directly reveal how many inches fell in a particular storm.

Scientists compare satellite estimates with ground gauges, snow boards, radar, and instruments that examine falling particles. This verification helps improve the calculations, but it is not a perfect inch-for-inch comparison: a ground station samples one location, while a satellite observes a larger area. NASA’s snowfall ground-validation work addresses these challenges.

What Actually Improves a Forecast?

Early seasonal snowfall outlooks try to predict the sum of storms that cannot yet be forecast individually. Seasonal precipitation is also generally harder to predict than seasonal temperature.

As winter approaches, newer ocean observations and climate-model runs can sharpen regional probabilities. As a storm approaches, satellites, weather balloons, surface stations, and radar clarify its track, moisture, and temperatures. During the storm, radar and local reports reveal where snow is falling and whether precipitation is changing type.

Better knowledge of the seasonal pattern improves the odds. Observing an actual storm improves the snowfall forecast.

When you read a forecast, ask which kind it is. A seasonal outlook offers context; a storm forecast supports local planning. Neither eliminates uncertainty—but each becomes useful when you understand what it can, and cannot, tell you.