The often difficult process of predicting accurate snowfall amounts is investigated in this paper by examining those physical ingredients which cause snowfall, and performing a verification study of three snow amount forecasting techniques: the Cook Method, the Magic Chart Method, and the Garcia Method. A case study approach was used to evaluate 40 snowstorm events which occurred in Kansas during three consecutive winters (1994 through 1996). The maximum snowfall totals for these events ranged from one to twelve inches. Each case was closely examined using observed data and NCEP model output to determine if the maximum snowfall amount could have been accurately forecasted 12 to 24 hours in advance using each of the three methods listed above. From this study, it was determined that the forecasting of snowfall amounts is largely a function of forecasting the ingredients which cause the snowfall, namely upward vertical motion in a given system, and the amount of water vapor available to that system to condense out. It will be shown that the success of using a snow amount forecasting technique is directly related to that technique's ability to account for the specific ingredients involved in a system. The results from this research indicate that the Garcia Method, which attempts to utilize the more important physical ingredients, can be a highly accurate and consistently reliable method of forecasting snowfall for many different types of snowstorms in the Great Plains. This paper also attempts to give some operational guidelines for analyzing and forecasting the necessary ingredients accurately. The ultimate goal of this paper is to improve the operational forecaster's skill in predicting snowfall amounts.

#National Weather Service, Topeka, Kansas