George Mason University’s (GMU) Center for Satellite and Earth Science Research (CSER) and NOAA’s Air Resources Lab have advanced NOAA’s Unified Forecast System-Air Quality Model (UFS-AQM) beyond its “Big-Leaf” canopy approximation by using detailed forest canopy processes and novel satellite data that can ultimately improve air quality predictions in the U.S. This critical enhancement helps resolve historical model biases in tropospheric ozone (i.e., “smog”), a key air pollutant with negative impact on human health, the ecosystem, and crop production, paving the way for the current and future community and operational air quality forecasting systems.
The subcanopy parameterizations and data are now fully merged into the UFS community’s Weather Model used within the Short-Range Weather Application (SRW-App). These and other updates are scheduled for operational implementation in the UFS-AQM Version 8, which forms the flagship National Weather Service/NOAA National Air Quality Forecasting Capability (NAQFC).
How It Was Done While vegetation is a known driver of surface atmospheric chemistry, chemical transport and air quality forecasting models have long neglected the complexities of the within forest canopy environment. By accounting for the fact that forests are more shaded and alter movement of chemical constituents, researchers unveiled the profound impact of canopy processes on the atmospheric boundary layer. These processes were split into three subcanopy layers in the UFS-AQM (Figure 1).
A novel Global Land Surface Dataset (GLSD) was derived at GMU from various satellite instruments at the resolution of 1 km globally and was used to describe the forest canopy regions in North America (Figure 2). Here researchers leveraged climatologies of forest canopy height, leaf area index, forest fraction, and clumping index. Additionally, gridded vertical profiles of cumulative leaf area index at 5 layers in the canopy derived from satellite- and ground-based data by collaborators at Environment and Climate Change Canada were used to determine the impact of the reduced light intensity due to canopy shading on atmospheric chemistry from above the canopy to the darkest levels near the surface.
The canopy shading impacted the photochemical ozone production during daytime by reducing the photolysis rates (i.e., darker forests, less sunlight, and less ozone production), and the canopy turbulence reduced the vertical mixing both day and night, by reducing the turbulent eddy diffusivities (diffusivity attenuations were strongest/weakest in the bottom/top canopy layer.) As a result, a shift occurred in the photochemical regime away from ozone production towards enhanced ozone destruction in the darkest, bottom-most canopy layer. The reduced subcanopy vertical mixing increased the atmospheric stability near the surface, especially at night, and trapped chemicals promoting ozone destruction.
When compared against near-surface U.S. EPA AirNow network, the resolved sub-canopy shading and turbulence corrected, in part, the long-standing systematic overpredictions in boundary layer ozone and fine particulate matter (PM2.5) for the UFS-AQM within and around contiguous forested regions over North America (Figure 3).
