Impact of Meteorological Inputs on Surface O 3 Prediction

1. Impact of Meteorological Inputs on Surface O3 Prediction Jianping Huang 9th CMAS Annual Conference Oct. 12, 2010, Chapel, NC

2. Co-Authors • Jeff McQueen1, Youhua Tang1,2, Binbin Zhou1,2, Marina Tsidulko1,2, Ho-Chun Huang1,2, Sarah Lu1,2, Brad Ferrier1,2, Bill Lapenta1, Geoff DiMego1(1: NOAA/NCEP/EMC, 2: IMSG) • Daewon Byun3, Pius Lee3, Daniel Tong3,4(3: NOAA/ARL, 4: ERT) • Ivanka Stajner (NOAA/NWS/OST)

3. Motivation and objectives • Motivation - O3 over-predicted especially by CB05 and in coastal regions • Objectives - to evaluate meteorological inputs - to reduce O3 over-prediction

4. Outline • National Air Quality Forecasting Capability • Current issue of O3 forecasting • Verification of meteorological inputs • Sensitivity ofO3 prediction to cloud parameters • Summary

5. Emission model: SMOKE - NEI 2005 - BEIS V3 Met model: WRF/NMM (NAM, 12 km/L60) - T, RH, Wind, etc. - Cloud, PBL (re-calculated by PreMAQ) AQ model: CMAQ (12km/L22) - Oper: CONUS(CB04), AK/HI(CB05/Aero-4) - Exper/Dev: CONUS(CB05/ Aero-4) National Air Quality Forecasting Capability http: www.weather.gov/aq

6. Current issue of O3 forecasting 5x (Exp.) 8-hr max O3 Aug-31-10 5x (Exp.) 8-hr max O3 Aug-31-10 ppb ppb 8-hr max O3 significantly over-predicted in NE coastal region as compared to AIRNOW 6

7. Current issue of O3 forecasting (cont.) O3 (ppb) fcst rmse obs bias Date (12 UTC Cycle) Date (12 UTC Cycle) Daily 8-hr max O3 (exp.) over-predicted (CONUS) Time period: July 1st to August 31st, 2010 7

8. Causes of O3 over-prediction • Emissions - NEI 2005 • Meteorological inputs - wind, etc. - cloud, PBL height (re-diagnosed in PreMAQ) • CMAQ - deposition velocity, etc. - CB05 mechanism • Lateral boundary condition - static

9. Verification tool and data • Verification tool: Forecast Verification System (FVS) - Grid2obs - Grid2grid - Statistics (e.g., rmse, bias) and FHO (e.g., csi, ets, far) • Met observational data - T, RH, Wind: ANYSFC, ADPUPA, ONLYSF, VADWND - Cloud: AFWA (global, 10 x 1o, 1-hr), CLAVR-x (global, 0.5o x 0.5o, 6-hr) • O3 data -AIRNOW • Studied time period - O3 and met verification: Jul. 1 to Aug. 31, 2010 - Sensitivity testing: Aug. 5 – 31, 2010

10. FVS statistics parameters FVS Statistics variables: F.H.O. F = grid fraction of forecasted > threshold O = grid faction of observed > threshold H = grid fraction of both forecasted and observed > threshold Basic statistics scores Bias=F/O=(a+b)/(a+c) Critical Success Index CSI=H/(F+O-H)=a/(a+b+c) Probability of Detection POD=H/O=a/(a+c) False Alarm Ratio FAR =1-H/F=b/(a+b) Thresholds: O3: > 55, 65, 75, 85, 105, 125, 150 (ppb) N=a+b+c+d d O=a+c c H=a F=a+b a b

11. Verification of met inputs Temperature (T) black: obs mean red: fcst mean Relative humidity (RH) RH (%) T (oC) Date Date rmse , bias of RH (%) rmse , bias of T (oC) black: rmse red: bias Date Date Domain: CONUS

12. Verification of met inputs (cont.) Wind speed (WS) black: obs mean red: fcst mean Cloud cover (%) TCLD (%) WS (m/s) Date Date rmse , bias of TCLD (%) rmse , bias of WS (m/s) black: rmse red: bias Domain: CONUS Date Date

13. How does cloud impact O3 prediction? Photolysis rate Jcld=J0[1+Cf(1.6trcos()-1] below cloud, Jcld=J0[1+Cfi(1-tr)cos()] above cloud, where J0 is the clear sky photolysis rate, Cf is cloud cover,  is the zenith angle, αi is a reaction dependent coefficient, and tr is cloud transmissivity, which is a function of cloud water content and cloud thickness. Cloud parameterization in PreMAQ - Cloud cover: Geleyn et al. (1982) (below PBL); Schumann (1989), Wyngaard and Brost (1984) (above PBL) - Liquid water content: Welcek and Taylor (1986), Change et al. (1987, 1990). NAM Cloud: more complicated cloud parameterization schemes (Ferrier et al. 2002) 13

14. Cloud cover FHO statistics False Alarm Ratio Critical Success Index False Alarm Ratio black: default red: modified % % Total cloud cover threshold Total cloud cover threshold Against AFWA for CONUS, Aug 05-31, 2010

15. Cloud cover FHO statistics (cont.) False Alarm Ratio Critical Success Index black: default red: modified % % Total cloud cover threshold Total cloud cover threshold Against CLAVR-x for CONUS, Aug 05-31, 2010

16. Sensitivity run: default vs. ModifiedPreMAQ Hourly-mean O3 difference (modified-default) ppb ppb 08-31-2010: 13 UTC 08-31-2010: 19 UTC

17. 8-hr max O3 verification: CONUS black dash: default-fcst red dash: modified-fcst solid: obs black: default red: modified solid: rmse dash: bias rmse , bias (ppb) fcst 8-hr max O3 (ppb) rmse obs bias Date (12 UTC Cycle) Date (12 UTC Cycle)

18. 8-hr max O3 verification: NEUS black: default red: modified solid: rmse dash: bias rmse , bias (ppb) 8-hr max O3 (ppb) black dash: default-fcst red dash: modified-fcst solid: obs Date (12 UTC Cycle) Date (12 UTC Cycle)

19. 8-hr max O3 FHO comparison: CONUS Critical Success Index black: default red: modified False Alarm Ratio ppb ppb 8-hr max O3 threshold 8-hr max O3 threshold (ppb)

20. 8-hr max O3 FHO comparison: NEUS black: default red: modified Critical Success Index False Alarm Ratio ppb ppb 8-hr max O3 threshold 8-hr max O3 threshold

21. Summary • O3 over-prediction is often observed especially near North-eastern coastal region. • Met verification results present that while temperature, relative humidity, and total cloud cover simulated by NAM show very good agreement with observations, NAM does not capture the time variability of the observed wind well. • The sensitivity study indicates that direct taking cloud parameters (cloud cover, liquid water content, cloud base and top) from NAM outputs may slightly improve surface O3 prediction especially over the NE coastal region.

22. Future work • The role of cloud parameters will be examined further in the coupling of the new NMMB meteorological model with CMAQ. • PBL schemes more suitable for stable atmospheric condition and marine boundary layer will be explored.