An Automated Synoptic typing Procedure to Predict Freezing Rain: An Application to Ottawa, Ontario

Freezing rain is a major weather hazard, which can compromise human safety, significantly disrupt transportation, and damage and disrupt built infrastructure such as telecommunication towers and transmission wires. In this study, an automated synoptic climatological procedure used principal components analysis, average linkage clustering procedure and discriminant function analysis to classify the weather types which were most likely to be associated with freezing rain events at the city of Ottawa, Ontario. Meteorological data that was used in the analysis included hourly surface observations from the Ottawa International Airport and 6 atmospheric levels of 6-hourly NCEP-NCAR upper-air reanalysis weather variables for the winter months (Nov.-Apr.) of 1958/59-2000/01. The data were divided into two parts: a developmental dataset (1958/59-1990/91) for construction (development) of the model and an independent dataset (1990/91-2000/01) for validation of the model. The procedure was able to successfully identify weather types that are most highly correlated with freezing rain events at Ottawa.

Stepwise logistic regression was performed on all days within the freezing rain weather categories to analytically determine the meteorological variables that can be used as forecast predictors for the likelihood of freezing rain occurrence at Ottawa. The results show that the model is best able to identify freezing rain events occurring several hours during a day. For example, in the independent or validation dataset, for likelihood values „d0.6, the procedure was able to identify 74% of all freezing rain events occurring 6 hrs or more during a day. Similarly, the procedure was able to identify 91% of all freezing rain events occurring 8 hrs or more during a day.