Parameterizing Mesoscale Wind Uncertainty for Dispersion Modeling

Abstract

A parameterization of numerical weather prediction uncertainty is presented for use by atmospheric transport and dispersion models. The theoretical development applies Taylor dispersion concepts to diagnose dispersion metrics from numerical wind field ensembles, where the ensemble variability approximates the wind field uncertainty. This analysis identifies persistent wind direction differences in the wind field ensemble as a leading source of enhanced ''virtual'' dispersion, and thus enhanced uncertainty for the ensemble-mean contaminant plume. This dispersion is characterized by the Lagrangian integral time scale for the gridresolved, large-scale, ''outer'' flow that is imposed through the initial and boundary conditions and by the ensemble deviation-velocity variance. Excellent agreement is demonstrated between an explicit ensemblemean contaminant plume generated from a Gaussian plume model applied to the individual wind field ensemble members and the modeled ensemble-mean plume formed from the one Gaussian plume simulation enhanced with the new ensemble dispersion metrics.

Conditional Performance Evaluation: Using Wildfire Observations for Systematic Fire Simulator Development

“…These have potential value for validation, model comparison and assisting with apportioning error for model calibration. In any situation, the poor performance of a model may be due to its design, the quality of the input data or operator error [52]; being able to discriminate between these is important for model improvement. However, there is limited research into optimal ways to apply performance metrics for model development.…”