Wednesday April 2, 2025
Desert Research Institute —
A new study published in Nature Communications utilizes insights gleaned from DRI’s Mountain Rain or Snow project to evaluate why traditional weather forecasting struggles to identify the rain/snow transition line. This forecasting is critical for informing communities about travel safety, accurate flood risk predictions, and managing water resources, but remains challenging when temperatures hover around the freezing point. The research was possible because thousands of community members across the U.S. contributed more than 40 thousand observations of the type of precipitation falling at their location. DRI scientists Meghan Collins, Anne Heggli, Sonia Tonino, Guo Yu, and Monica Arienzo contributed to the research.
“This work is a natural extension of our previously published study, which used the citizen science data to validate the performance of three precipitation phase products from NASA,” said Guo Yu, Assistant Research Professor of Hydrometeorology at DRI and coauthor of the research. “In this new study, we further demonstrate that even the most advanced machine learning methods do not perform well in distinguishing between rain and snow without the incorporation of novel data sources.”