Improving precipitation forecasts over complex terrain using numerical weather prediction and analog ensemble methods

The work in this dissertation enhances precipitation forecast skill with a focus on southwest British Columbia (BC), Canada. Local weather predictions in this region can be subject to large uncertainties because of the complex terrain and the upstream data void. Electricity production in BC relies heavily on hydropower. Thus, accurate precipitation forecasts are crucial to manage water resources and mitigate flood risks. There are two major components to a skillful prediction system: (1) numerical weather prediction (NWP), and (2) post-processing of the NWP output. First, this work investigates sensitivities of precipitation performance to configurations of the Weather Research and Forecasting (WRF) model. Skill varies by model grid spacing, parameterization selections, location, season, precipitation intensity, and accumulation period. An evaluation of over 100 systematically varied WRF configurations provides insight to precipitation forecasting challenges and shows that the optimal model setup depends on the weather situation and the verification metric most important to end users. A few of the best performing model configurations are then post-processed using the analog ensemble (AnEn) method. This statistical method derives a future forecast by searching an archive of past model forecasts for similar (analog) conditions, and then collects the corresponding past observations into an ensemble. This dissertation utilizes existing and new optimization techniques to significantly improve AnEn computational efficiency and forecast skill. The detection of good analogs is improved through predictor weighting methods and consideration of temporal predictor trends and supplemental lead times. Finally, this work proposes new ensemble generation techniques. Applying the past analog error to the target forecast reduces the AnEn dry bias and makes prediction of high-impact (heavy-precipitation) events more reliable. A multi-model AnEn further improves predictive skill, but at higher computational cost. The AnEn performance shows larger sensitivity to the grid spacing of the NWP than to the physics configuration. The final ensemble prediction system provides skillful and reliable high-resolution forecasts across all precipitation intensities.Science, Faculty ofEarth, Ocean and Atmospheric Sciences, Department ofGraduat