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