Probabilistic Forecasting Methods of Winter Mixed Precipitation Events in New York State Utilizing a Random Forest

Abstract

Winter mixed precipitation events are associated with multiple hazards and create forecast challenges due to the difficulty in determining the timing and amount of each precipitation type. In New York State, complex terrain enhances these forecast challenges. Machine learning is a relatively nascent tool that can help improve forecasting by synthesizing large amounts of data and finding underlying relationships. This study uses a random forest machine learning algorithm that generates probabilistic winter precipitation type forecasts. Random forest configuration, testing, and development methods are presented to show how this tool can be applied to operational forecasting. Dataset generation and variation are also explained due to their essential nature in the random forest. Lastly, the methodology of transitioning a machine learning algorithm from research to operations is discussed.