Turbulent Snow Transport and Accumulation: New Reduced-Order Models and Diagnostics

TL;DR

This paper introduces new reduced-order models and diagnostics for simulating turbulent snow transport, addressing challenges in accurately predicting snow particle dynamics influenced by atmospheric turbulence.

Contribution

It couples modern snow particle drag models with nonlinear dynamical systems reduction techniques to create a simplified, physically meaningful framework for snow transport modeling.

Findings

Developed reduced-order models with quantifiable errors

Integrated novel diagnostics for snow accumulation analysis

Enhanced stability and physical interpretability of snow transport simulations

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. Even with perfectly resolved wind fields, accurately predicting the fate of mobile snow particles in wind relies on semi-empirical assumptions embedded in diffeo-integro equations that contain numerical instabilities. The present research couples a modern approach to snow particle drag with model order reduction tools from nonlinear dynamical systems. Coupled with novel accumulation diagnostics, we provide a simplified framework of snow transport with well-defined simplification errors and rigorous physical meaning.