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

This is the authors' abstract. We don't add key points for this paper.

Winter mixed-precipitation events are associated with multiple hazards and create forecast challenges that are 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 because of their essential nature in the random forest. Last, the methodology of transitioning a machine learning algorithm from research to operations is discussed. Significance Statement Examining the role that machine learning can play in winter precipitation type forecasting is an area of research that has ample room for exploration, as much of the previous research has focused on applying machine learning to warm-season precipitation and severe weather events. Establishing a framework and methodology to successfully combine machine learning and weather research into effective operational tools is a valuable addition to the machine learning community. Because machine learning is increasingly being applied to meteorology, this work can act as a road map to help develop other meteorological tools based in machine learning.