A JFSP Fire Science Exchange Network
Bringing People Together & Sharing Knowledge in the Northern Rockies

Predicting smoke impacts with uncertain emissions and meteorology

Date: March 4, 2014
Presenter(s): Talat Odman

Smoke from wildland fires can have adverse impacts on visibility and also on public health. Models are available for simulating the dispersion, long-range transport, and chemical evolution of fire plumes and predicting their impacts on air quality. However, these models are not perfect tools for decision making purposes. There are uncertainties in their formulations of complex atmospheric phenomena and the input data they utilize are also subject to various levels of uncertainty. More importantly, the level of uncertainty in their predictions is not well understood. While it is difficult to quantify the uncertainty in model predictions it is possible to study the sensitivities to various modeling parameters. Emission estimates and meteorological parameters are two major categories of smoke models inputs and the sensitivities of model predictions to them will be evaluated in this presentation. Uncertainties in bottom-up emission calculations will be illustrated for a case study where detailed fuel and emission data were collected. The sensitivities of downwind smoke predictions to the magnitude, injection height and time rate of emissions will be analyzed. The results of modeling with bottom-up emission estimates will be compared to those obtained using satellite-based emission estimates. The sensitivities to wind speed and wind direction will also be analyzed and compared to emission related sensitivities. The goal of these analyses is to better understand the leading factors of uncertainty in smoke modeling and point to where the efforts should be concentrated for uncertainty reduction.

Topic(s): Fire Effects, Ecological - First Order, Emissions, Smoke & Air Quality, Smoke & Populations, Smoke Emissions
Ecosystem(s): None
Type: Webinar
NRFSN number: 12840
FRAMES RCS number: 16890
Record updated: Dec 21, 2017