NOAA’s Next-Generation Air Quality Predictions for the United States
Bibliographic record
Abstract
NOAA is developing the next generation air quality (AQ) prediction system for the United States (U.S.) and global aerosol predictions within the Unified Forecast System (UFS) framework to better represent and forecast impacts of wildfires on AQ and impacts of aerosols globally on weather from hourly to subseasonal scales. A new regional UFS weather model is online coupled with chemistry represented by the EPA’s Community Multiscale AQ (CMAQ) modeling system with Carbon Bond VI and AERO6 mechanisms to form this new UFS-AQM system. Wildfire emissions are specified by satellite-observed hourly Regional Hourly Advanced Baseline Imager (ABI) and Visible Infrared Imaging Radiometer Suite (VIIRS) Emissions (RAVE). Anthropogenic emissions are based on U.S. EPA’s National Emissions Inventories over the contiguous 48 U.S. states and global inventories elsewhere. Lateral boundary conditions for aerosols are provided by NOAA’s Global Ensemble Forecast System which includes the Goddard Chemistry Aerosol Radiation and Transport (GOCART) module. A bias correction post-processing procedure is included in UFS-AQM to improve prediction accuracy. Testing is performed over a large regional domain covering the U.S., and evaluation is done in near-real time and for retrospective periods. Recent examples indicate much improved representation of impacts of wildfires on AQ predictions, especially during Quebec fires in the summer of 2023. Some of the planned refinements for UFS-AQM include better representation of plume rise for wildfire smoke and for point source emissions, increased resolution consistent with the Rapid Refresh Forecast System (RRFS), which is under development, and using aerosol lateral boundary conditions from a 6-way coupled atmosphere - ocean - land - sea-ice - waves - aerosols global UFS system, also under development. Due to challenging computational requirements for UFS-AQM at high resolution, a machine learning emulator is being developed to improve computational efficiency for prediction of chemical transformations and tracer transport. Of most interest for this session, data assimilation capabilities are being developed to constrain initial conditions for pollutant concentrations in UFS-AQM. Observations being assimilated include fine particulate matter (PM2.5) observations from AirNow, VIIRS Aerosol Optical Depth (AOD) retrievals and TROPOspheric Monitoring Instrument (TROPOMI) NO2 retrievals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".