Improving prediction of mountain snowfall in the southwestern United States using machine learning methods

Snowfall forecasting has historically been an area of difficulty for operational meteorologists, particularly in regions of complex terrain, such as the western United States. Attempts at improving forecasts have been made, but skill is still poor, with snowfall routinely overpredicted. A major reason for this overprediction has been the failure to accurately predict snow-liquid ratios (SLR) ahead of major events. This research proposes, develops, and tests multiple machine learning methods for dynamic SLR prediction for the Sky Islands of southeast Arizona by objectively comparing a multiple linear regression (MLR) against several more complex and flexible machine learning methods. Input parameters for each method were chosen based on variables found by previous studies to have a regression-based relationship with SLR, with a focus on the lower mid-levels of the troposphere. These parameters were also used to construct the MLR model, and its performance was compared objectively with the machine learning methods. When tested on historical events, a very high percentage of the network-predicted SLR values fall within the margin of error of observed SLRs, which were calculated using gridded snow depth and snow water equivalent (SWE) data from the University of Arizona daily 4-km SWE, SD, and SCE dataset (UASnow). A support vector machine (SVM), a k-nearest neighbor (KNN) algorithm, and a random forest also showed high accuracies when tested on the dataset, and each showed a significant gain in skill compared with the MLR model, with skill being evaluated by multiple metrics.