Investigating the role of temporal resolution and multi-model ensemble data on WRF/XGB integrated snowfall prediction for the Northeast United States

Predicting snowfall accumulation in the Northeast United States, especially during extreme weather events like rapidly intensifying storms, is a significant challenge. Accurate snowfall predictions are crucial for public safety, infrastructure planning, and economic stability, yet they are difficult to achieve due to the complex processes of snowfall formation and forecasting. This study focused on the impact of enhancing temporal resolution and integrating multi-model ensemble data to improve snowfall prediction accuracy. We combined outputs from the Weather Research and Forecasting (WRF) model with machine learning (ML) algorithms and gridded snowfall products. Specifically, we explored the impact of finer temporal resolutions, such as 6-hour versus 24-hour feature intervals, on snowfall predictions and examined the impact of incorporating ensemble snowfall data that provided various percentile snowfall accumulations and probabilities of exceeding specific thresholds. Results demonstrated that the 6-hour model significantly reduced the overall prediction error, particularly during rapidly intensifying storms, by up to 30%. The inclusion of ensemble data further enhanced the prediction of 24-hour snowfall, particularly in reducing the bias. Despite these advancements, challenges persist in accurately forecasting heavy snowfall amounts and capturing complex atmospheric dynamics during extreme events.