The Synoptic Program focused on air quality research and forecasting in its FY25 NOFO. It prioritized the following capabilities:
- Develop and evaluate high-resolution air quality forecast capabilities consistent with NOAA weather forecast models.
- Evaluate the National Air Quality Forecast Capability system consisting of a UFS-based regional model coupled with an online Environmental Protection Agency chemistry model.
- Improve spatial and temporal estimates of human-made and natural pollutant emissions.
- Explore and quantify the potential value of ensemble model approaches, post-processing, and AI to NOAA’s operational air quality forecasting guidance.
- Improve model accuracy using data assimilation of remotely-sensed products or in-situ observations.
- Develop verification software, methods, and techniques to ensure air quality forecast capabilities.
- Optimize chemistry processes to increase computational efficiency.
| Project Title | Principal Investigators | Project Description |
|---|---|---|
| Implement the DeepCTM to Enhance the Performance of the National Air Quality Forecast Capability | Jia Xing, University of Tennessee Youhua Tang, George Mason University | Integrate DeepCTM — a cutting-edge machine-learning model designed to emulate chemical transport models (CTMs) — with NOAA’s operational National Air Quality Forecast Capability (NAQFC). This is expected to enhance the forecasting of atmospheric chemical concentrations. |
