Near-surface temperature variability is influenced by geographic and terrain characteristics. My research examines how these influences vary by weather type. This knowledge is used to determine the best methods for modelling temperature in the mountains and prairies in southwestern Alberta, using data collected as part of the Foothills Climate Array (FCA) study.
A weather classification system was developed for the area using multivariate statistical analysis, and six weather patterns were identified. Missing temperature data in the FCA are gap-filled using regression equations generated using the most closely correlated station for each site, where correlations are calculated by seasonal weather type. Seasonal weather type correlations improve estimates by ~7% over monthly correlations. The biggest improvements (10 to 20%) occur for chinook and cool-wet days. Cold Arctic air days and hot anticyclonic days in summer show the lowest improvement, indicating strong within-type variability for these weather types. These weather types also show the most variable temperature lapse rates, with frequent inversions.
Local weighted regression models outperform multivariate regression models by between 4 and 8% in the mountains. Daily temperature and elevation are not always strongly correlated, most notably during Arctic cold spells. This is true for both minimum and maximum temperatures in the mountains. Therefore, regression models using elevation as the only predictor perform poorly, particularly in winter months. Vertical and horizontal separation are the most important factors in choosing local neighbours, with vertical separation being most important for minimum temperatures and for winter months. Relative elevation and slope, as indictors of cold air pooling potential, influence the selection of local neighbours for minimum and mean temperature models.
Spatial proximity is the most important factor determining temperature relatedness in the prairies. Minimum temperatures are strongly influenced by urban and relative elevation effects. Sites located within the city of Calgary are warmer than those in the outlying areas, and temperatures are warmer away from low lying areas. Seasonal variability is stronger than weather type variability in the prairies. Therefore, kriging is suggested as an appropriate method for estimating temperature in the prairies, with models parameterised monthly.