Impact of Meteorological Inputs on Surface O 3 Prediction Jianping Huang 9 th CMAS Annual Conference Oct. 12, 2010, Chapel, NC.

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Presentation transcript:

Impact of Meteorological Inputs on Surface O 3 Prediction Jianping Huang 9 th CMAS Annual Conference Oct. 12, 2010, Chapel, NC

Co-Authors Jeff McQueen 1, Youhua Tang 1,2, Binbin Zhou 1,2, Marina Tsidulko 1,2, Ho-Chun Huang 1,2, Sarah Lu 1,2, Brad Ferrier 1,2, Bill Lapenta 1, Geoff DiMego 1 (1: NOAA/NCEP/EMC, 2: IMSG) Daewon Byun 3, Pius Lee 3, Daniel Tong 3,4 (3: NOAA/ARL, 4: ERT) Ivanka Stajner (NOAA/NWS/OST)

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

Outline National Air Quality Forecasting Capability Current issue of O 3 forecasting Verification of meteorological inputs Sensitivity ofO 3 prediction to cloud parameters Summary

National Air Quality Forecasting Capability Emission model: SMOKE - NEI 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) http:

Current issue of O 3 forecasting 6 8-hr max O 3 significantly over-predicted in NE coastal region as compared to AIRNOW 5x (Exp.) 8-hr max O 3 Aug ppb

Current issue of O 3 forecasting (cont.) 7 Daily 8-hr max O 3 (exp.) over-predicted (CONUS) Time period: July 1 st to August 31 st, 2010 O 3 (ppb) Date (12 UTC Cycle) obs fcs t bias rmse

Emissions - NEI 2005 Meteorological inputs - wind, etc. - cloud, PBL height (re-diagnosed in PreMAQ) CMAQ - deposition velocity, etc. - CB05 mechanism Lateral boundary condition - static Causes of O 3 over-prediction

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, 1 0 x 1 o, 1-hr), CLAVR-x (global, 0.5 o x 0.5 o, 6-hr) O 3 data - AIRNOW Studied time period - O 3 and met verification: Jul. 1 to Aug. 31, Sensitivity testing: Aug. 5 – 31, 2010

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: O 3 : > 55, 65, 75, 85, 105, 125, 150 (ppb) N=a+b+c+d F=a+b O=a+c H=a b c d a

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

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

How does cloud impact O 3 prediction? Photolysis rate J cld =J 0 [1+C f (1.6t r cos(  )-1] below cloud, J cld =J 0 [1+C f  i (1-t r )cos(  )] above cloud, where J 0 is the clear sky photolysis rate, C f is cloud cover,  is the zenith angle, α i is a reaction dependent coefficient, and t r 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

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

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

Sensitivity run: default vs. Modified PreMAQ : 13 UTC : 19 UTC Hourly-mean O 3 difference (modified-default) ppb

8-hr max O 3 verification: CONUS black dash: default-fcst red dash: modified-fcst solid: obs obs fcst rmse bias Date (12 UTC Cycle) black: default red: modified solid: rmse dash: bias 8-hr max O 3 (ppb) rmse, bias (ppb)

8-hr max O 3 verification: NEUS 8-hr max O 3 (ppb) black: default red: modified solid: rmse dash: bias black dash: default-fcst red dash: modified-fcst solid: obs Date (12 UTC Cycle) rmse, bias (ppb)

8-hr max O 3 FHO comparison: CONUS 8-hr max O 3 threshold Critical Success Index False Alarm Ratio black: default red: modified 8-hr max O 3 threshold (ppb) ppb

8-hr max O 3 FHO comparison: NEUS 8-hr max O 3 threshold Critical Success Index black: default red: modified False Alarm Ratio ppb

Summary O 3 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 O 3 prediction especially over the NE coastal region.

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.