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Abstract Recent studies have used numerical models to estimate the collection efficiency of solid precipitation gauges when exposed to the wind in both shielded and unshielded configurations. The models used computational fluid dynamics (CFD) simulations of the airflow pattern generated by the aerodynamic response to the gauge–shield geometry. These are used as initial conditions to perform Lagrangian tracking of solid precipitation particles. Validation of the results against field observations yielded similarities in the overall behavior, but the model output only approximately reproduced the dependence of the experimental collection efficiency on wind speed. This paper presents an improved snowflake trajectory modeling scheme due to the inclusion of a dynamically determined drag coefficient. The drag coefficient was estimated using the local Reynolds number as derived from CFD simulations within a time-independent Reynolds-averaged Navier–Stokes approach. The proposed dynamic model greatly improves the consistency of results with the field observations recently obtained at the Marshall Field winter precipitation test bed in Boulder, Colorado.
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Abstract Accurate snowfall measurements are necessary for meteorology, hydrology, and climate research. Typical uses include creating and calibrating gridded precipitation products, the verification of model simulations, driving hydrologic models, input into aircraft deicing processes, and estimating streamflow runoff in the spring. These applications are significantly impacted by errors in solid precipitation measurements. The recent WMO Solid Precipitation Intercomparison Experiment (SPICE) attempted to characterize and reduce some of the measurement uncertainties through an international effort involving 15 countries utilizing over 20 types and models of precipitation gauges from various manufacturers. Key results from WMO-SPICE are presented herein. Recent work and future research opportunities that build on the results of WMO-SPICE are also highlighted.