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CCPP SciDoc for UFS-SRW v3.0.0
SRW v3.0.0
Common Community Physics Package Developed at DTC
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CCPP SciDoc for UFS-SRW v3.0.0
SRW v3.0.0
Common Community Physics Package Developed at DTC
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The SRW v3.0.0 release includes the smoke modeling capability. In the model, a single aerosol tracer (smoke) represents primary fine particulate matter (PM2.5) from biomass burning (BB) emissions. The BB emissions are estimated using the fire radiative energy data from the regional ABI and VIIRS Emissions (RAVE) product (Li et al. 2022 [119]), which integrates fire radiative power (FRP) data from the Geostationary Operational Environmental Satellites (GOES-R ABI) and the Joint Polar Satellite System (JPSS VIIRS). The FRP data from RAVE is used to estimate the fire heat flux in SRW to simulate the fire plume rise in the model. The SRW application allows users to select predefined grids for smoke emission processing, and the smoke model forecast in the SRW supports most of the predefined grids such as 25, 13 and 3 km resolution grids, which cover CONUS and North America regions. The appropriate fixed smoke model files have been staged on Tier 1 platforms to support these forecast grids. The model incorporates parameterizations for fire plume rise, wet and dry removal processes and radiative feedback of smoke. Additionally, the feedback of smoke on the surface visibility diagnostics is included in the postprocessing part of the SRW application.
The model outputs a new fire weather index - hourly wildfire potential (HWP). The HWP index is calculated based on air temperature, humidity, wind speed and soil moisture (James et al., in review).
Smoke simulations can be performed for three different scenarios: (1) useing BB emissions for the same day and hour as observed, which are estimated from satelllite observations for the current day; (2) persistence, where BB emissions are estimated using satellite observations from the previous day; and (3) modulated persistence, where BB emissions are forecasted based on the HWP index simulated by the model.
The model predicts 3D BB emissions and concentrations of smoke, and other diagnostics variables such as HWP, vertically integrated smoke and others. The following table provides full list of variables available in GRIB2 files, which are generated by the SRW v3.0.0:
| Variable | Description | Unit |
|---|---|---|
| AEMFLX | Smoke Emissions | kg/m^2/s |
| WFIREPOT (or var discipline=2 master_table=2 parmcat=4 parm=26) | hourly wildfire potential index | unitless |
| COLMD: entire atmosphere (considered as a single layer):anl:chemical=Particulate Organic Matter Dry | vertically integrated smoke | kg/m^2 |
| MASSDEN:8 m above ground:anl:chemical=Particulate Organic Matter Dry:aerosol_size<2.5e-6 | near surface smoke | kg/m^3 |
| MASSDEN:8 m above ground:1-2 hour ave fcst:chemical:Particulate Organic Matter Dry:aerosol_size<2.5e-06 | 1-h avg near surface smoke (PM2.5) | kg/m^3 |
| AOTK | aerosol optical depth | unitless |
Aerosols have both direct and indirect effects on meteorology, atmospheric chemistry, human health, and ultimately the global energy budget with dust being a major contributor to the atmospheric aerosol burden. A great effort has been made to characterize the sources and mobilization of dust, however, current models still show large uncertainty as the modeling of mineral dust in the atmosphere is complex.
The FENGSHA dust emission model, implemented into the operational NOAA National Air Quality Forecast Capability (NAQFC), and the new Global Ensemble Forecast System with Aerosols (GEFS-Aerosols), is a flexible emission model capable of predicting dust emissions across forecast scales.
The FENGSHA dust emission scheme, developed by NOAA ARL, simulates mineral dust emissions using a combination of theoretical frameworks and empirical measurements. FENGSHA builds upon the MB95 dust emission model by integrating Dale Gillette's observations, emphasizing the role of soil characteristics - such as texture, moisture, and roughness - in determining dry threshold friction velocities. This scheme enhances predictions of dust aerosol concentrations, providing instrumental in applications like air quality forecasting and climate modeling.
FENGSHA merges theoretical concepts from Marticorena and Bergametti (1995) [138] with empirical data to model dust emissions more accurately. The main horizontal dust flux uses the Webb et al. (2020) [202] flux equation:
\[ Q = C \frac{\rho_{a}}{g}(R u_{*}^3) \left ( 1 - \frac{u_{*t}^2(R,H)}{u_{*}^2} \right ) \left ( 1 + \frac{u_{*t}(R,H)}{u_{*}} \right ) \]
FENGSHA is implemented in CCPP with modular subroutines for compatibility with other atmospheric components. Key routines include:
The scheme facilitates seamless interaction with other physical processes, including aerosol microphysical and radiation models, ensuring consistency in atmospheric simulations. FENGSHA'S modular structure allows its outputs — binned surface emissions — to integrate smoothly into broader forecasting frameworks.
FENGSHA is employed in operational systems like GEFS-Aerosol(Zhang et al. 2021 [218]) and NAQFC (Huang et al. 2025, Li et al., 2024), enhancing air quility and dust event predictions. It provides detailed insights into the interactions between soil properties and atmospheric dynamics.
1.9.8