Improving Mountain Snowfall Forecasts in the Southwestern US using Machine Learning Methods

Abstract

Snowfall forecasting has historically been an area of difficulty for operational meteorologists, particularly in the remote complex terrain of the Western US. Attempts at improving forecasts have been made, but skill is still poor, with snowfall routinely overpredicted. A major reason for this overprediction has been a 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 in the form of a simple feed-forward neural network, a support vector machine, and a k-nearest neighbor algorithm. Input parameters 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 a multiple linear regression model, and its performance was compared with the machine learning methods. Each of the machine learning methods showed significant improvement compared to the multiple linear regression. When tested on historical events, nearly 95% of the network-predicted SLR values fell within the margin of error of observed SLRs, calculated using verification data from Zeng et al (2018), with slightly higher accuracies for both the SVM and KNN algorithms. Each showed significant gain in skill compared to the multiple linear regression model. Current and future work is focusing on shifting to higher resolution data, as well as adjusting the model to look at a wider region within the mountainous Western US in order to achieve greater operational benefit.