Verification of mesoscale numerical weather forecasts in mountainous terrain for application to avalanche prediction

摘要

Two high-resolution, real-time, numerical weather prediction (NWP) models are verified against case study observations to quantify their accuracy and skill in the mountainous terrain of western Canada. These models, run daily at the University of British Columbia (UBC), are the Mesoscale Compressible Community (MC2) Model and the University of Wisconsin Nonhydrostatic Modeling System (NMS). The main motivations of this work are: 1) to extend the lead time of avalanche forecasts by using NWP-projected meteorological variables as input to statistical avalanche threat models; and 2) to create another tool to help avalanche forecasters in their daily decision-making process. Observation data from the Whistler/Blackcomb ski area in the British Columbia ( BC) Coast Mountains and from Kootenay Pass in the Columbia Mountains of southeast BC are used to verify the forecasts. The two models are run with grid spacings of 3.3 km ( MC2) and 10 km ( NMS) over Whistler/Blackcomb, and with 2, 10 ( MC2), and 30 km ( NMS) over Kootenay Pass. The quality of the forecasts is measured using standard statistical methods for those variables that are important for avalanche forecasting. It is found that the raw model output has biases that can be easily removed using Kalman filter predictor postprocessing. The resulting automatically corrected forecasts have quite small absolute errors in temperature (0.7degreesC). It is also found that the coarser-resolution NMS model produces comparable results to the finer-resolution MC2 model for precipitation at Kootenay Pass. These objective forecast errors are of the same order of magnitude as the meteorological observation (sampling/representativeness) errors in the snowy, windy mountainous terrain, resulting in forecasts that have value in extending the range of avalanche forecasts for locations such as Kootenay Pass, as discussed in a recent study by Roeger et al. [References: 20]