Evaluation of the 2006 Air Quality Forecasting Operation in Georgia

1. Evaluation of the 2006 Air Quality Forecasting Operation in Georgia Talat Odman , Yongtao Hu, Ted Russell School of Civil & Environmental Engineering Michael Chang, Carlos Cardelino School of Earth and Atmospheric Sciences Georgia Institute of Technology 5th Annual CMAS Conference October 17, 2006 Georgia Institute of Technology

2. Air Quality Forecasting • There is an increasing interest in day-to-day variation of air quality • Public becoming more health conscious • Local authorities looking for short-term management strategies • Forecasts are produced using various techniques • Persistence • Climatology • Statistical Regression • Close Neighbor • Decision Tree • 3-D Air Quality Models Georgia Institute of Technology

3. Air Quality Forecasting in Atlanta • Ozone forecasting since 1996 Olympic Games • Panel of experts gets together and issues a forecast for next day • Ozone Alerts • One of the methods used is 3-D AQM • Urban Airshed Model (UAM) • Diagnostic Meteorology • Constant Emissions • Arguably first in the U.S. but now mostly outdated • Last year, PM2.5 forecasting started • Forecasts being extended to other cities in Georgia • Macon (~135 km South-Southeast of Atlanta) Georgia Institute of Technology

4. Goal of our Operation • To provide accurate, “fine-scale”, local forecasts sufficiently in advance that • Local controls can be triggered to avoid bad episodes • Personal exposure can be minimized • NOAA/EPA’s national forecast currently provides 12-km resolution over the Southeast • We are currently at 4-km resolution aiming at 1-km or “finer” resolution locally. • Our goal is to forecast not just air quality but effectiveness of predetermined local control strategies Georgia Institute of Technology

5. Our Modeling System • WRF for meteorology • Driven by NAM (formerly Eta) • 3 ½ -day NAM forecasts available every 6 hours (00, 06, 12, 18Z) • SMOKE for emissions • Forecasting of EGU, mobile, biogenic emissions • CMAQ for chemistry and transport • Currently using standard version 4.5 • Will start using our contributions soon: • Direct Decoupled Method (DDM-3D) for emission sensitivities • Variable Time Step Algorithm (VARTSTEP) for speed • Dynamic Solution Adaptive Grid Algorithm (DSAGA) for high resolution Georgia Institute of Technology

6. Modeling Domain and Grids • Three grids: • 36-km (72x72) • 12-km (72x72) • 4-km (99x78) • Horizontal domains are slightly larger for WRF • 34 vertical layers used in WRF • 13 layers in CMAQ Georgia Institute of Technology

7. Our 2006 Operation • Started May 1, 2006 (through September 30th) • Tomorrow’s forecast is due by 10 a.m. today • Processing takes 1 ½ days • Wednesday’s forecasting starts by Sunday night • We simulate: • 3 days over the 36-km grid using 00Z NAM, IC from previous cycle (warm start) and “clean” BC • 2 ½ days over the 12-km grid using 12Z NAM and IC/BC from 36-km • 24 hours over the 4-km using 12Z NAM and IC/BC from 12-km • Mostly automated • 1 hr/day of human interaction • 6 CPUs • The product is a 24-hr ozone and PM2.5 forecast once per day Georgia Institute of Technology

8. Evaluation • 11 stations measuring O3 every hour • 6 stations measuring PM2.5 mass every hour Georgia Institute of Technology

9. O3 in Metro Atlanta: Summer of 2006 Georgia Institute of Technology

10. Performance Metrics Forecast Observations Georgia Institute of Technology

11. O3 Performance: 4-km vs. EPD’s Our 4-km Forecast EPD Ensemble Forecast Georgia Institute of Technology

12. O3 Performance: 4-km vs. 12-km Our 4-km Forecast Our 12-km Forecast Georgia Institute of Technology

13. O3 Bias & Error by Site Georgia Institute of Technology

14. Forecastedvs. Observed O3 Georgia Institute of Technology

15. O3 Performance until July 20 Our 4-km Forecast EPD Ensemble Forecast Georgia Institute of Technology

16. O3 Performance: Parts I and II Georgia Institute of Technology

17. O3 at Gwinnett on July 5, 2006 Observed 8-hr: 91 ppb Forecasted 8-hr : 89 ppb Georgia Institute of Technology

18. PM2.5 in Metro Atlanta: Summer of 2006 Georgia Institute of Technology

19. PM2.5 Bias & Error by Site Georgia Institute of Technology

20. Forecastedvs. Observed PM2.5 Georgia Institute of Technology

21. PM2.5 Performance: May-Aug. and Sept. Georgia Institute of Technology

22. PM2.5 at South Dekalb on Sep. 11, 2006 • Obs. 24-hr: 32.6 mg/m3 4-km 24-hr : 28.7 mg/m3 Georgia Institute of Technology

23. Conclusion • We started a “fine-scale” forecasting operation in GA using 3-D models • Spatial variability of O3 and PM2.5 indicates the need for finer scales • The 4-km forecast is slightly more accurate than the 12-km forecast • Predictions at some sites are better than others, especially for PM2.5 • Ozone forecasts were generally accurate until mid-July (no bias, 20% error) but overestimations dominated afterwards (30% bias, 40% error) • Diurnal changes are somewhat captured; daily peaks are generally underestimated • PM2.5 was generally underestimated May-August (40% bias, 40% error) but more accurate in September (-5% bias, 25% error) • Afternoon peaks were generally missed • Spatial variability was underestimated both for O3 and PM2.5 Georgia Institute of Technology

24. Next Steps • Continue the operation in 2007 • Improve accuracy • Extend the domain of coverage • Increase the resolution • Elongate the forecasting period • Issue daily updates • Link the forecast to health-effects studies: • Study the impacts on asthmatic children • Build a data archive for long-term exposure studies • Forecast the effectiveness of short-term, local control strategies • Predict the impacts of predetermined strategies Georgia Institute of Technology

25. Acknowledgement This research is supported by: • Georgia Department of Natural Resources • Air Resources Engineering Center at Georgia Tech Georgia Institute of Technology