The distribution of SLR at Alta is found to be similar to that observed at lower elevations in the surrounding region, with substantial variability throughout the winter season. Significant high- and low-SLR events are characterized by notably different temporal evolutions of the local thermal and wind profile and regional synoptic pattern. Using data from the North American Regional Analysis, temperature, wind speed, and mid-level relative humidity at various levels are found to affect SLR, with the strongest correlation occurring between SLR and mean crest level (650 hPa) temperature. A stepwise multiple linear regression equation is constructed to account for a large portion of the SLR variance.
Finally, archived National Centers for Environmental Prediction 40-km Eta 12-36 h forecasts are used to develop model output statistics for predicting SLR. This approach is used to produce hindcasts of SLR at Alta for multiple winter seasons and yields an improvement over several existing operational SLR prediction techniques, although errors in model QPF over complex terrain are still shown to limit skill in forecasting snowfall amount.
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