Title: Seasonal snowfall prediction in the United States using multiple discriminant analysis
Description: Seasonal snowfall is the focus of this work due to its impact on human society and health. This study seeks to create a first ever seasonal snowfall forecast using Multiple Discriminant Analysis. Winter total snowfall amount and frequency of snowfall events was predicted. The independent variables were in the form of ocean-atmosphere teleconnection patterns, large-scale atmospheric patterns, land cover, and temperature. Results not only confirm relationships previously documented between atmospheric phenomena and United States snowfall, but it also expands our understanding of factors that influence decadal-scale snowfall variation. Some of the independent variables newly discovered to be connected with snowfall are Arctic sea ice extent, and Eurasian snow cover extent. Other variables identified and influential, but have previously been seen in literature are the ENSO, PNA, NAO, AO and TSA. A comparison between regional and station forecasts shows the regional forecasts to be skillful as or better than the station level forecasts. However, there is a large amount of variability in station snowfall, which indicates a limit to the performance of any regional forecast. Seasonal snowfall forecasts are made for six regions in the United States and for 440 individual snowfall stations over the time period 1930 to 2006. Based on cross-validation of the model using a jack knife method, the snowfall forecasts are correct 20% to 70% of the time.

This study analyzes the ability of a statistical forecast model based on Multiple Discriminant Analysis to predict winter snowfall frequency and amount. Several large-scale atmospheric variables and teleconnection patterns are included as independent predictors, such as the PNA, ENSO, fall temperature, Arctic sea ice extent, and others. Seasonal snowfall forecasts are made for six regions in the United States and for 440 individual snowfall stations over the time period 1930 to 2006. Results not only confirm relationships previously documented between atmospheric phenomena and United States snowfall, they also expand our understanding of factors that influence decadal-scale snowfall variation by including variables such as Eurasian snow cover extent and Arctic sea ice extent. Based on cross-validation of the model using a jack knife method, the snowfall forecasts are correct 20% to 70% of the time.
Subjects: Snow, Weather forecasting, Discriminant analysis
Places: United States