Classificatory performance evaluation of air quality forecasting in Georgia

1. Classificatory performance evaluation of air quality forecasting in Georgia Yongtao Hu1, M. Talat Odman1, Michael E. Chang2 and Armistead G. Russell1 1School of Civil & Environmental Engineering, 2Brook Byers Institute of Sustainable Systems Georgia Institute of Technology 9th Annual CMAS Conference, October 12th, 2010

2. Overview • The Hi-Res air quality forecasting system • O3 and PM2.5 performance for metro Atlanta • New SOA module and its impact on PM2.5 performance • Classificatory performance evaluation: linking forecast performance to weather types and emissions conditions

3. Hi-Res: forecasting ozone and PM2.5 at 4-km resolution for metro areas in Georgia Georgia Institute of Technology

4. Hi-Res Forecast Products Snapshots from Hi-Res homepage: http://forecast.ce.gatech.edu • “Single Value” Report: tomorrow’s AQI, ozone and PM2.5 by metro area in Georgia • Air Quality Forecasts: AQI, ozone and PM2.5, 48-hrs spatial plots and station profiles • Meteorological Forecasts: precipitation, temperature and winds, 48-hrs spatial plots and station profiles • Performance Evaluation: time series comparison and scatter plots for the previous day Georgia Institute of Technology

5. Evolution of Hi-Res during 2006-2010 • Updated to latest release of WRF each year before the ozone season. • WRF 2.1, 2.2, 3.0, 3.1 and 3.2 • CMAQ is typically one version behind. • CMAQ 4.6 with Georgia Tech extensions • Projected NEI to current year in the very beginning of each year. • Updated forecast products website each year before ozone season. • Switched from single-cycle forecasting to two-cycles in 2008. • Enlarged 4-km domain to cover the entire state of Georgia in 2009. • Introduced Georgia Tech’s new SOA module in 2009.

6. Ambient Monitoring Sites for Performance Evaluation

7. Performance Metrics NAAQS Forecast NAAQS Observation

8. Overall 2006-2010 Performance (Ozone Season): Atlanta Metro PM2.5 Ozone Georgia Institute of Technology

9. Ozone Performance

10. Forecastvs. Observed O3 2008 2007 2010 2009 Georgia Institute of Technology

11. 2009 O3 Performance: Hi-Res vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast Economic down turn?

12. 2010 O3 Performance: Hi-Res vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast Economic recovery?

13. PM2.5 Performance

14. Summer

15. Forecastvs. Observed PM2.5 2007 2008 2009 2010 Georgia Institute of Technology

16. 2009 PM2.5 Performance: 4-km vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast Economic down turn? New SOA module?

17. 2010 PM2.5 Performance: 4-km vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast New SOA module?

18. Aging process: +OH,+O3 Aerosol +OH,+O3 LSVOC HSVOC New SOA Module (Baek, J., Georgia Tech, 2009) SOA species in CMAQ: Included processes: • SOA partitioned from anthropogenic VOCs’ oxidations (8 SVOCs) • From monoterpenes (2 SVOCs) • From isoprene (2 SVOCs added) • From sesquiterpenes (1 SVOC added, gas phase oxidation reactions added for α-caryphyllene, β-humulene, and other sesquiterpenes) • Aging of all semi-volatile organic carbons (SVOCs)added • AORGAJ and AORGAI • AORGBJ and AORGBI • AORGBISJ and AORGBISI • AORGBSQJ and AORGBSQI • AORGAGJ and AORGAGI

19. Forecastvs. Observed OC at South DeKalb Ozone Season May-September OC PM2.5 2009

20. Met. Performance

21. Overall 2006-2010 Performance (Ozone Season): Atlanta Metro Humidity Temperature Georgia Institute of Technology

22. Forecastvs. Observed Temperature Georgia Institute of Technology

23. Forecastvs. Observed Humidity Georgia Institute of Technology

24. Classificatory Performance Evaluation during 2006-2009 Hu, Y., et al. 2010, "Using synoptic classification to evaluate an operational air quality forecasting system in Atlanta," Atmos. Pol. Res., 1, 280-287.

25. Spatial Synoptic Classification (Sheridan 2002, http://sheridan.geog.kent.edu/ssc.html): Atlanta Calendar(ozone season) Weather Types • DP (dry polar): lowest in temp, clear and dry. • DM (dry moderate): air is mild and dry. • DT (dry tropical): hottest and driest. • MP (moist polar): cloudy, humid and cool. • MM (moist moderate): warmer and more humid. • MT (moist tropical): warm and very humid. • TR (transitional): one type yields to another, large shifts in P,Td,V.

26. Forecasting error versus observation for spatial synoptic classifications *Statistics are for 2006-2009 summers where not specified.

27. Observations Grouped by Weather Type PM2.5 Ozone Georgia Institute of Technology

28. 8-hr O3 Performance Grouped by Weather Type 2009 2006-2008

29. 24-hr PM2.5 Performance by Weather Type 2006-2008 2009

30. GA EPD forecasting Performance by Weather Type Ozone PM2.5

31. Linking Performance to Emissions Conditions Ozone PM2.5 • Emissions Conditions Classification: • Special weekdays (Monday and Friday) • Typical weekdays • Weekends/holidays

32. Summary • 2006-2010 ozone forecasts are good. • Overall bias is +16% and error is 23% • 2006-2008 PM2.5 forecasts are not very accurate. • May-September bias is -37% and error is 43% • The new SOA module improved 2009 and 2010 PM2.5 performances • May-September bias is 8% and error is 25% for 2009 • May-September bias is 4% and error is 21% for 2010 • 2006-2010 temperature and humidity performances are good. • Better ozone performance and, during 2009-2010, better PM2.5 performance are associated with dry weather types. • Forecast performance is worse on weekend/holidays Georgia Institute of Technology

33. Acknowledgements We thank Georgia EPD for funding the Hi-Res forecasts, Our former group member Dr. Jaemeen Baek for the new SOA module, and Dr. Carlos Cardelino of Georgia Tech for team forecasts.

34. Forecastvs. Observed 2006 PM2.5 O3 Temperature Humidity Georgia Institute of Technology

35. Winter

36. Forecastedvs. Observed PM2.5 2007 2008 2009