A simple physically based snowfall algorithm

The era of highly detailed digital forecasts has raised client expectations and their demand for detailed and skillful quantitative snowfall forecasts (QSF). The challenge of accurate QSF given improved NWP quantitative precipitation forecasts (QPF) lies in the ability to accurately assess the snow ratio and its evolution throughout a winter storm.

Snow crystal density is primarily a function of temperature and humidity. Highly spatial or low density crystals such as dendrites are favored at temperatures between -12 and -18 Celsius. This temperature zone is commonly referred to as the snow production zone (SPZ). Low density crystals are further favored in supersaturated environments as would be expected in at least moderately ascending air. In a basic sense the observed crystal density or snow ratio is at any given time the result of a natural integration of the temperature and humidity along the trajectory of the falling crystal. Observed snowfall is then the integration of the product of snow ratio and precipitation rate over time.

The snowfall algorithm, developed by the author, mimics nature by using a top-down approach to calculate snow ratios at given times using NWP data sets. Layer contributions to snow ratio are a function of temperature, vertical motion, and relative humidity. In brief, the average temperature of each humid layer (RH>75%) is equated to a snow ratio. The snow ratio is then weighted by the layer's percent contribution to the total column upward motion. Finally, the snow ratio contribution of each layer is summed up to yield a sub-cloud base snow ratio.

The highest snow ratios, 25:1, will occur when the bulk of the vertical motion lies within the SPZ. Smaller snow ratios occur when the vertical motion maxima does not coincide with the SPZ, or when the vertical motion maxima stretches vertically from the SPZ encompassing both warmer or colder temperatures.

The snowfall algorithm can be used with both Bufkit and as a Smart Tool in the NWS's Graphical Forecast Editor (GFE). Bufkit data has the advantage of having the highest vertical resolution possible and consequently the best estimate at a given point of snow ratio and potential snowfall. It also allows a forecaster to evaluate the snow ratio through visual inspection of the sounding. The GFE Smart Tool has the advantage of producing aerial distributions of snow ratio and snowfall. The best forecast would be made using the two together.

This presentation will briefly review snow microphysics as it relates to snow ratio and the methodology of the algorithm. Application of example output from both the Bufkit and Smart Tool versions will be demonstrated using a case which occurred over northern Maine on January 22, 2004. Finally, efforts towards a comprehensive verification of the algorithm for the upcoming winter season will be introduced.