Machine learning shows a limit to rain-snow partitioning accuracy when using near-surface meteorology

Keith S. Jennings (), Meghan Collins, Benjamin J. Hatchett, Anne Heggli, Nayoung Hur, Sonia Tonino, Anne W. Nolin, Guo Yu, Wei Zhang and Monica M. Arienzo
Additional contact information
Keith S. Jennings: 210 Colchester Ave
Meghan Collins: Desert Research Institute
Benjamin J. Hatchett: Desert Research Institute
Anne Heggli: Desert Research Institute
Nayoung Hur: Lynker
Sonia Tonino: Desert Research Institute
Anne W. Nolin: Reno
Guo Yu: Desert Research Institute
Wei Zhang: Soils & Climate
Monica M. Arienzo: Desert Research Institute

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract Partitioning precipitation into rain and snow with near-surface meteorology is a well-known challenge. However, whether a limit exists to its potential performance remains unknown. Here, we evaluate this possibility by applying a set of benchmark precipitation phase partitioning methods plus three machine learning (ML) models (an artificial neural network, random forest, and XGBoost) to two independent datasets: 38.5 thousand crowdsourced observations and 17.8 million synoptic meteorology reports. The ML methods provide negligible improvements over the best benchmarks, increasing accuracy only by up to 0.6% and reducing rain and snow biases by up to -4.7%. ML methods fail to identify mixed precipitation and sub-freezing rainfall events, while expressing their worst accuracy values from 1.0 °C–2.5 °C. A potential cause of these shortcomings is the air temperature overlap in rain and snow distributions (peaking between 1.0 °C–1.6 °C), which expresses a significant negative relationship (p

Date: 2025
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DOI: 10.1038/s41467-025-58234-2

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