PNNL

Abstract

Coupling between mesoscale models and large-eddy simulations (LES) is increasingly used to more realistically represent the wide range of scales of atmospheric motions aecting boundary layer winds and turbulence that need to be simulated accurately for applications such as wind energy. However, such mesoscale-to-microscale coupled modeling frame works are potentially aected by a large number of uncertain closure parameters. Here we investigate the sensitivity associated with six closure parameters related to a 1.5-order subgrid-scale turbulence closure for an ensemble of mesoscale-coupled LES. The simulations are performed using the Weather Research and Forecasting model nested from horizontal resolutions of greater than a kilometer down to tens of meters. Closure parameters are varied to generate perturbed parameter ensembles for two case studies of highly sheared, convective boundary layers observed in the Columbia Basin of Oregon and Washington during the Second Wind Forecast Improvement Project (WFIP2). Machine learning algorithms such as random forest and gradient boosting are used to explore the sensitivity of LES predictions, considering the eects of the perturbed physical parameters alongside categorical factors such as the case study identity, measurement location, and LES resolution. For the conditions we examine, a single parameter, the eddy viscosity coecient, is the dominant source of parametric sensitivity and its importance is comparable to the categorical factors for many of the simulation response variables we examine.