Development of Canopy-App for Atmospheric Composition Modeling Across Scales

The vegetation canopies in the NOAA Unified Forecast System (UFS) weather and atmospheric composition modeling suite, like in many other similar regional and global models, are only treated as a 2-D surface (i.e., “big-leaf” approach) and typically only affect the atmosphere via the boundary fluxes (e.g., emission and deposition). The dynamical and chemical environment within the in-canopy airspace, however, can be quite different from the atmosphere above, and is generally characterized by less light and reduced vertical mixing, among other factors. Here we introduce the “canopy-app” (https://github.com/noaa-oar-arl/canopy-app), a development effort not only for stand-alone analysis but ultimately as a model component under NOAA UFS for weather and atmospheric composition applications at various temporal and spatial scales (rather than a full 1-D canopy model applied typically at site-scale). It is built on established canopy parameterizations and represents the complex vertically-resolved, leaf-scale canopy effects by computing select variables and adjustment factors used to efficiently modulate the relevant atmospheric quantities, such as the gas-phase chemical species. The canopy effects currently included are: photolysis attenuation, vertical turbulent diffusivity, biogenic emissions, and an in-canopy wind adjustment factor for fire spread and, potentially, for other applications. The canopy-app’s modular design allows selectively and efficiently choosing the desired canopy effect. At its inception, it includes relatively simplified parameterizations, but it is continuously developed to advance the model algorithms and include other processes important for atmospheric composition predictions (e.g., dry deposition, soil/canopy exchange, etc.). Here we demonstrate two canopy-app uses: i) offline, using point/tower data and gridded atmospheric analyses and forecasts, and ii) online, coupled with box models and prototypes integrated within the NOAA UFS.