The need for a modeling system capable of simulating mean and turbulent components of flow through a canopy as well as accounting for the impact of vegetation on the surface energy budget, motivated a series of modifications to the Advanced Regional Prediction System (ARPS) atmospheric model. The effects of vegetation elements (e.g., branches, leaves) on simulated wind flow through a forest canopy have been accounted for by adding a drag force term to the momentum equation, and by adding a sink term to the turbulent kinetic energy equation to account for more efficient dissipation of turbulence within the canopy. The impact of the canopy on the surface energy budget has been accounted for by computing net radiation flux at canopy top and prescribing an exponential decay of net radiation inside the canopy, to emulate the attenuation of net radiation by vegetation elements. As a final step in the development process, ARPS has been coupled to the Pacific Northwest National Laboratory (PNNL) Integrated Lagrangian Transport (PILT) model.
Fine-scale atmospheric dispersion modeling systems, including PILT-ARPS, are currently being validated using pre-existing datasets and data from a recent prescribed burn in the New Jersey Pine Barrens. The meteorological component of PILT-ARPS has recently been validated against data from the CHATS (Canopy Horizontal Array Turbulence Study) experiment and the results of this validation are presented here. A series of numerical experiments have been conducted to simulate momentum and scalar fields in and above the vegetation canopy, both prior to and following the seasonal growth of leaves. Sensitivity of results to various factors has also been examined, including surface drag, turbulence parameterization, canopy morphology, and grid structure. Findings gleaned from this exercise are intended to provide guidance regarding model parameter specification for real-time modeling applications. This work is part of a larger Joint Fire Science Program (JFSP) project focused on the development and validation of modeling tools for predicting smoke dispersion from low-intensity fires.