Turbulent snow transport and accumulation: New reduced‐order models and diagnostics

Abstract Understanding and modeling snow particle dynamics in the atmosphere remains a significant challenge for atmospheric scientists, hydrologists, and glaciologists. Temporally and spatially varying rates of snow transport, deposition, and erosion are driven by atmospheric turbulence and further complicated by inertial particle dynamics. The present research takes a modern approach to predict snow particle motion with model order reduction tools from nonlinear dynamical systems. A modified Maxey–Riley equation with nonlinear drag is introduced that faithfully provides terminal particle velocities and the characteristic sweeping and loitering particle behavior as seen in decades of hydrometeor studies. A geometric model order reduction of this equation is derived and provides up to 100 times more efficient Lagrangian snow particle trajectory simulations with comparable accuracy. Lastly, novel accumulation diagnostics based on the reduced‐order model provide a simplified framework of snow transport with well‐defined simplification errors and rigorous physical meaning. The accuracy of these diagnostics is verified both numerically and in large‐eddy simulations against terrestrial laser scans at an Arctic‐alpine study site.