Predicting and forecasting wildfire smoke requires the integration of fire behavior, vegetation mapping, and atmospheric circulation. While there isn’t an active operational model called “GWEM,” the Global Wildland Fire Emission Model (GWEM) is a heavily cited, process-based computational model initially developed by the Max Planck Institute for Meteorology to calculate atmospheric emissions from biomass burning. It serves as a foundational “emissions engine” for larger atmospheric and chemical transport models.
To understand how these tools predict smoke, it helps to look at the core components that models like GWEM utilize to calculate how smoke is generated, where it goes, and how it impacts air quality:
1. The Emissions Source: How Smoke is Calculated (The GWEM Approach)
Predicting smoke requires first knowing how much material burned and what was released. The GWEM model uses a bottom-up approach to calculate the total mass of pollutants emitted by looking at four specific variables:
Area Burnt (A): GWEM utilizes satellite-derived maps—such as the European Space Agency’s GLOBSCAR and active fire hotspots from the Moderate Resolution Imaging Spectroradiometer (MODIS)—to detect the spatial extent of the fires.
Available Fuel Load (AFL): Models require knowledge of what is burning (e.g., boreal forest vs. tropical savanna). GWEM links its burned area data to the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) to estimate the mass of dry, burnable plant matter per square kilometer.
Burning Efficiency (β): The fraction of the fuel load that actually combusts and turns into smoke rather than just smoldering. Emission Factors ( Efcap E sub f
): Different ecosystems emit different chemical concoctions. GWEM factors in the specific chemical species released (e.g., PM2.5, Carbon Monoxide, Nitrogen Oxides, and Black Carbon). 2. Plume Rise: Injecting Smoke into the Sky US Forest Service Research and Development (.gov) Modelling smoke transport from wildland fires: a review
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