Predicting snow is rocket science

A man braces his umbrella while walking through the wind and snow in New York City on Thursday, February 13.

A man walks along the snow-covered Vietnam Veterans Memorial in Washington on February 13.

A woman covers her face while walking in New York City on February 13.

A worker clears a snowy sidewalk in Washington on February 13.

Cars sit covered in snow on a street in Manassas, Virginia, on February 13.

A man in Philadelphia shields his face from the elements February 13.

A snowplow removes snow from the sidewalk in Washington's Lafayette Park, across the street from the White House, on February 13.

People walk through snow February 13 in the Chinatown neighborhood of New York City.

Kevin Miller looks out the passenger window of his friend's car as they sit stuck in traffic during a winter storm in Raleigh, North Carolina, on Wednesday, February 12.

Traffic moves slowly along Wade Avenue in Raleigh on February 12. Motorists were encouraged to stay off roads.

Katharine Newton, a student at the University of North Carolina at Chapel Hill, calls her parents February 12 while walking more than two miles from campus to her parents' home.

Snowplows clear Interstate 75/85 in downtown Atlanta on February 12.

A downed power line lays across several vehicles in Atlanta on February 12.

A police officer redirects traffic in Charlotte, North Carolina, on February 12.

Ice coats trees hanging over a sign for the Broadway at the Beach tourist attraction in Myrtle Beach, South Carolina, on February 12.

A sign warns drivers of winter weather as they travel on a bleak section of Highway 141 in Norcross, Georgia, on February 12.

A truck in Bossier City, Louisiana, blocks access to Interstate 220, which was closed because of icy conditions on February 12.

City workers spread a mixture of sand and salt on an intersection in Avondale Estates, Georgia, on February 12.

Ice and snow cover Interstate 26 in Columbia, South Carolina, on February 12.

Shmetrice Moore, a nurse at an Emory University hospital in Johns Creek, Georgia, scrapes snow and ice off her windshield as she and others are released early from their shift on February 12.

Hossam Shalaby waits for his rescheduled flight under a departure board at Hartsfield-Jackson Atlanta International Airport on Tuesday, February 11.

A vehicle travels in Greenville, South Carolina, on February 11.

People shop for what is left at a Publix grocery store in Decatur, Georgia, on February 11.

Vehicles slowly make their way over a snow-covered Route 35 in Fort Payne, Alabama, on February 11.

Weather data is projected onto the face of Clint Perkins, director of state operations for the Georgia Emergency Management Agency, as he works in Atlanta on February 11.

A vehicle drives through falling snow on the U.S. 421 bypass in Sanford, North Carolina, on February 11.

Ice and snow storms are crippling some states in the Southeast

Ian Roulstone: Predicting how much snow will actually accumulate is difficult

He says the physics of snowfall is complex, involving factors like temperature, moisture

Roulstone: Making the final call comes down to "seat of the pants" knowledge in the end

Editor's note: Ian Roulstone is professor of mathematics at the University of Surrey in England. He is co-author of "Invisible in the Storm: The Role of Mathematics in Understanding Weather" (Princeton University Press).

(CNN) -- A snow-covered landscape is one of the classic images showcasing the beauty of weather on Earth. We are awed by the grandeur of white-capped mountains and the almost magical quality of snow-covered trees. We are also frustrated when the tempests of winter reach far and wide, striking as they have done this year in America's southern states.

When it comes to forecasting the likelihood of a blizzard, the weather anchors know what to say. But when asked to predict how much snow will actually accumulate, they will give estimates. Why?

The computer models used in weather forecasting do not actually predict snow. A single variable is used to predict water in its various forms -- liquid, vapor or ice -- so we need other information, such as air temperature, to decide whether snowfall is likely.

Models forecast the amount of liquid water produced when air rises above the height at which water vapor begins to condense. This is commonly known as the Quantitative Precipitation Forecast (QPF). While QPFs are an important ingredient when it comes to predicting snow, there are many other very subtle factors that can easily tip the balance in favor of rain, or make it very difficult to distinguish between different types of snow.

If temperatures are low enough to allow precipitation to fall as snow, then the forecasters need some way to convert the QPF to an equivalent snowfall. The ratio that we use to calculate the liquid water to snow equivalent is around 1 to 10. That is, if the QPF predicts 1 inch of rain, we can anticipate the amount of snow produced would be 10 inches.

Unfortunately, life's not always that simple.

The liquid water to snow ratio can vary depending on whether the snow is "wet" or "dry." Dry snow is the term used to describe small powdery flakes, and it forms when there is very little moisture available. Under these circumstances, the rain to snow ratio can be considerably higher, with values of 1 to 20 not uncommon.

On the other hand, if there is abundant moisture and the snowflakes are larger and wetter, a ratio of 1 to 5 may be typical. Therefore we need to have a very accurate forecast of the levels of moisture in the atmosphere, together with the variation of temperature with altitude, to even get off on the right foot when it comes to predicting snowfall.

Our forecast models represent a snapshot of the weather at any moment in time by using huge arrays of numbers to describe states of the atmosphere. These numbers represent basic variables such as moisture and temperature.

Calculating how these many millions of "weather pixels" will change requires superfast computation and large amounts of memory. The inevitable limitations on available computer power and data storage force a trade-off between the geographical coverage of models and the detail that we can expect from them. This trade-off can be critical when it comes to calculating reliable QPFs.

Forecasters often have to resort to methods they learned at college, involving dew points, temperature soundings from meteorological balloon ascents and real-time reports from weather stations to assess the impact of a snow storm.

The bottom line is that snow -- one of our most loved, and occasionally loathed, features of weather -- is real tough to forecast well.

We may be able to capture the beauty of Mother Nature with high resolution digital images, but capturing the physics behind the snowfall in our sophisticated weather prediction models is much more challenging.

Making the final call comes down to "seat of the pants" knowledge in the end.

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The opinions expressed in this commentary are solely those of Ian Roulstone.