Wind predictions in complex terrain are important for a number of reasons including: fire management, post-fire wind erosion, pheromone dispersion related to insect control, and wind energy applications. Terrain effects such as wind speed-up over ridges, flow channeling in valleys, flow separation around terrain obstacles, and enhanced surface roughness from vegetation alter the flow field. In turn, these processes affect scalar transport and diffusion and complicate modeling of near-surface concentrations. In this paper we describe a scalar transport algorithm within a high-resolution wind model, WindNinja, for modeling near-surface transport of scalars.

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Introduction

Scalar Transport and Dispersion in Complex Terrain within a High-Resolution Mass-Consistent Wind Modeling Framework

Natalie Wagenbrenner, Steve Edburg, Brian Lamb, Laboratory for Atmospheric Research, Washington State University, Pullman, WA 99164Jason Forthofer, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory, US Forest Service, Missoula, MT 59808

Inputs

Mass-consistent wind modelTerrain-following coordinate system with

hexahedral cell meshVariational calculus approach used with finite

element method to solve governing equation:

WindNinja

Digital elevation model

Vegetation type Trees Shrubs Grass

Output Options

Scalar Transport

KMZ files for visualization in Google Earth Raster/vector files for GIS programs VTK files for 3-D visualizations

WindNinja predictions for Big Southern Butte, ID

WindNinja predictions compared to field observations

Features Atmospheric stability

Continued Work Evaluation of solvers for advection-diffusion Field validation of predicted concentrations

Post-fire PM10

Pheromones

Advection-diffusion equation:

This work is supported by the USDI and USDA Forest Service Joint Fire Sciences Program.

Diurnal slope flow PM10 emissions algorithm

Incident solar radiation is used to determine stability effects and diurnal wind flows.

https://collab.firelab.org/software/projects/windninja

𝜕𝜕𝑥 (𝑅𝑥

𝜕Φ𝜕 𝑥 )+ 𝜕

𝜕 𝑦 (𝑅𝑦𝜕Φ𝜕 𝑦 )+ 𝜕

𝜕𝑧 (𝑅𝑧𝜕Φ𝜕 𝑧 )+𝐻=0

, , = eddy diffusivities = 0.4 zz = height above ground = 2, = u, v, w wind components = Scalar concentration = source term

, , = Gauss precision moduli (stability effects) = Potential flow = source term (based on initial wind field)

WRF predictions (1.3 km) and WRF-initialized WindNinja predictions (138 m) for Big Southern Butte, ID.

Wind input Domain Average Point initialization Mesoscale forecasts

― WRF, NAM, RAP, GFS

Resolution Simulation Time (seconds)

Coarse (~ 150 m) 10Medium (~ 100 m) 25Fine (~ 70 m) 35

Conjugate gradient solver

Fast run times

Table 1. Approximate simulation times for different mesh resolutions

Simulation times are for a typical 20 x 20 km domain on an average laptop/desktop computer.

WindNinja predictions for Big Southern Butte, ID under neutral and stable atmospheric conditions.

Source initialization Lat/long, source strength, release height

Single point Multiple points: csv format Area source: raster format

Solvers: Bi-Conjugate Gradient Generalized Minimal Residual

𝐹 𝑣=𝐾 𝜌𝑔 𝑢∗ (𝑢∗

2 −𝑢∗𝑡2 )

= vertical flux of PM10

= PM10 release rate of soil = air density, = friction velocity, threshold friction velocity

Rz calculated for layer 10 in the mesh (20 to 80 m above ground).

Evaluations of predicted flow fields High-resolution wind measurements

from a 2010 field campaign

Post-fire PM10 area source.

𝜕𝜕𝑥 (𝑅𝑥

𝜕Φ𝜕 𝑥 )+ 𝜕

𝜕 𝑦 (𝑅𝑦𝜕Φ𝜕 𝑦 )+ 𝜕

𝜕𝑧 (𝑅𝑧𝜕Φ𝜕 𝑧 )+𝐵𝑥

𝜕Φ𝜕𝑥 +𝐵𝑦

𝜕Φ𝜕 𝑦 +𝐵𝑧

𝜕Φ𝜕 𝑧 +𝐻=0

Flow is generally up and over butte. Flow separation around butte.