2024/12/03 by Andrew Charbonneau, Charbonneau, Andrew, Katherine M. Deck +3
Earth and Planetary Sciences · Medicine · #Atmospheric and Oceanic Physics (physics.ao-ph) #Cryospheric studies and observations #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Meteorological Phenomena and Simulations #Winter Sports Injuries and Performance
paper · pdf · doi:10.48550/arxiv.2412.06819
openalex publication_date 2024/12/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02
This paper presents a physics-constrained neural differential equation framework for parameterization, and employs it to model the time evolution of seasonal snow depth given hydrometeorological forcings. When trained on data from multiple SNOTEL sites, the parameterization predicts daily snow depth with under 9% median error and Nash Sutcliffe Efficiencies over 0.94 across a wide variety of snow climates. The parameterization also generalizes to new sites not seen during training, which is not often true for calibrated snow models. Requiring the parameterization to predict snow water equivalent in addition to snow depth only increases error to ~12%. The structure of the approach guarantees the satisfaction of physical constraints, enables these constraints during model training, and allows modeling at different temporal resolutions without additional retraining of the parameterization. These benefits hold potential in climate modeling, and could extend to other dynamical systems with physical constraints.