A Statistical Forecast Model for Extratropical Cyclones Including Intensity and Precipitation Type

Abstract

Abstract Winter extratropical cyclones (ETCs) are dominant features of winter weather on the east coast of North America. These storms are characterized by high winds and heavy precipitation (rain, snow, and ice). ETCs are well predicted by numerical weather prediction models (NWPs) at short- to midrange forecast lead times, but prediction on seasonal time scales is lacking. We develop a set of multiple linear regression models, using stepwise regression and cross validation, to predict the number of storms expected to affect a specific location throughout the winter storm season. Each model in the set predicts a specific storm type (e.g., snow, rain, or bomb storms). This set of models is applied in a probabilistic forecast framework that uses the probability density function of the prediction in combination with climatological mean storm activity. The resulting forecast makes statements about the likelihood of below-average, average, or above-average activity for all storms and for each of the type-specific subsets of storms. Though this forecast framework could in theory be applied anywhere, we demonstrate its skill in forecasting the characteristics of the winter storm season experienced in Halifax, Nova Scotia, Canada. Significance Statement Winter storms are a disruptive but inevitable part of life on the eastern coast of North America all the way from the Carolinas to Labrador. Knowing each fall what to expect for the upcoming winter storm season is not only a matter of public interest, but also of great public safety and financial importance. Here we develop a model that uses the state of the atmosphere over the month of September to forecast the upcoming winter storm characteristics for a specified region of interest. Our model uses a multiple linear regression approach to make skilled forecasts including probability statements about the level and type of storm activity. Forecasts can be used to inform planning for the winter ahead.