1 Impact on Ozone Prediction at a Fine Grid Resolution: An Examination of Nudging Analysis and PBL Schemes in Meteorological Model Yunhee Kim, Joshua S.

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1 Impact on Ozone Prediction at a Fine Grid Resolution: An Examination of Nudging Analysis and PBL Schemes in Meteorological Model Yunhee Kim, Joshua S. Fu, and Terry L. Miller University of Tennessee, Knoxville Department of Civil & Environmental Engineering

2 Outline Background and ObjectiveBackground and Objective Model Configurations and DescriptionsModel Configurations and Descriptions Sensitivity to INTERPPXSensitivity to INTERPPX Sensitivity to PBL Schemes and Analysis NudgingSensitivity to PBL Schemes and Analysis Nudging ConclusionsConclusions

3 SIPs (State Implementation Plans) for Nonattainment Areas Any area that does not meet the national primary or secondary ambient air quality standard for the pollutant. Demonstrate the ozone attainment in these nonattainmnet areas by SIPs. In 1997, NAAQS (National Ambient Air Quality Standards) for 8-hour Ozone of 85ppb was set up.

4 Nonattainment Areas in East Tennessee In East Tennessee, 7 counties are nonattainment for ozone

5 Continued. New NAAQS for 8-hr O3 was revised from 85 ppb to 75 ppb as May 27, (It will result in increased nonattainmnet areas in the United States) US EPA recommend that using 4km horizontal grid cells may be desirable for urban and fine scale portions of nested regional grids. 1 However, studies have also shown that finer grid resolutions do not always give better performance because of the complexity in chemistry and meteorology. 2 Generally, the meteorological model performance for temperature predicts well at finer horizontal grid resolution in terms of overall-wide statistics and area-specific statistics while wind speed tend to overpredict at most areas US EPA, Cohan et al 2006; Zhang et al., 2006a,b; Wu et al., Cohan et al., 2006; Barna et al., 2000; Zhang et al.,2006a; Wu et al., 2008

6 Objective To provide the better model performance in complex terrain and improve daily maximum 8- hr ozone concentrations at finer grid resolutions for SIPs

7 MM5 Configurations and Descriptions Horizontal Grid Resolution:36-km/12-km/4-kmHorizontal Grid Resolution:36-km/12-km/4-km Vertical Grid Resolution: 34 layersVertical Grid Resolution: 34 layers Simulation Period: May 15– September 15, 2002Simulation Period: May 15– September 15, 2002 MM5 (v.3.7) Options:MM5 (v.3.7) Options: PX, Eta M-Y (Mellor-Yamada) –PBL: PX, Eta M-Y (Mellor-Yamada) MRF (Medium Range Forecast) –LSM:PX, NOAH –Cumulus: KF2 (Kain and Fritsch) –Moisture:Mixed phase –Radiation: RRTM (rapid radiative transfer model)

8 CMAQ Configurations and Descriptions Model Domain Descriptions:Model Domain Descriptions: –Nestdown from VISTAS’s 12km –121 x 114 grids, 19 layers –CMAQ 4.5 with CBIV mechanism –Initial & Boundary Condition: VISTAS 12-km obtained from VISTAS VISTAS 12-km obtained from VISTAS CON US 36-km VISTAS 12-km ETN 4-km

9 Simulation Descriptions :Descriptions: : –Emissions: Typical 2002 BaseG Emissions obtained from VISTAS Typical 2002 BaseG Emissions obtained from VISTAS –SMOKE2.1 used –For Base case : Area, Nonroad, Mobile, Point, Fire and Biogenic emissions –For Sensitivity : Mobile, Point, and Biogenic emissions to rerun –INTERPPX for PX LSM –Analysis nudging (PX and NOAH)

10 Methodology 1. Step – Test INTERPPX w/ and w/o on PX LSM 2. Step – Test PX and Noah LSM 3. Step – Test with Analysis nudging 3D FDDA + INTERPPX 3D & Surface FDDA + INTERPPX 3D & Surface FDDA w/o INTERPPX PXNoah_EtaNoah_MRF Analysis Nudging with 2.5, 4.5, 6.0 x10 -4 /sec for winds

11 1. Step - INTERPPX 4-km INTERPPX Simulations4-km INTERPPX Simulations INTERPPX is a new preprocessor used to initialize soil moisture, temperature, and canopy moisture from a previous VISTAS 12-km MM5 run. 3DINT 3DFDDA w/INTERPPX BDINT 3DFDDA + Surface FDDA w/INTERPPX BDPX 3DFDDA+ Surface FDDA w/o INTERPPXSimulationFDDA INTERPPX Option 3DINT 3D- FDDA O BDINT 3D & Surface FDDA O BDPX 3D & Surface FDDA X

12 Results from INTERPPX 3D INT - 3DFDDA + INPERPPX BDINT - 3DFDDA + Surface FDDA W/ INTERPPX BDPX - 3DFDDA + Surface FDDA W/O INTERPPX ValleyMountain

13 Results from INTERPPX Valley Mountain At valley, BDINT predicts well for wind speed. BDPX predicts well for temperature. At mountain, all of three overpredict temperature and wind speed. 3D INT - 3DFDDA + INPERPPX BDINT - 3DFDDA + Surface FDDA W/ INTERPPX BDPX - 3DFDDA + Surface FDDA W/O INTERPPX

14 O 3 Time series and Statistics for O 3 BDINT performed better than BDPX So BDINT was selected 3D INT - 3DFDDA + INPERPPX BDINT - 3DFDDA + Surface FDDA W/ INTERPPX BDPX - 3DFDDA + Surface FDDA W/O INTERPPX

15 2. Step - Sensitivity to PBL 4-km PBL Sensitivity Simulations Baseline: PXBaseline: PX PBL Sensitivity: N_E, N_MPBL Sensitivity: N_E, N_M SimulationLSMPBL PXPXPX N_ENOAHEta N_MNOAHMRF Valley PX– PX PBL + INTERPPX N_E – Noah Eta PBL N_M – Noah MRF PBL Mountain

16 Sensitivity to PBL Statistics for Meteorology ValleyMountain PX– PX PBL + INTERPPX N_E – Noah Eta PBL N_M – Noah MRF PBL

17 Sensitivity to PBL Spatial & Temporal Distribution of Max 8-hr O3

18 2. Step -Summary At valley, Noah_MRF shows the lowest bias of wind speed and Noah_Eta predicts temperature well. At mountain area, Noah Eta alone predicts wind speed well but none of them predicts well for temperature. PX and N_M show good model performance at valley while N_E shows model performance well at mountain area. PX– PX PBL + INTERPPX N_E – Noah Eta PBL N_M – Noah MRF PBL

19 3. Step - Sensitivity to Analysis Nudging Analysis Nudging SimulationsAnalysis Nudging Simulations *3D Analysis & Surface : nudging with winds, temp, and water mixing ratio

20 Sensitivity to Analysis Nudging Time series and Statistics for Meteorology ValleyMountain PX_a:PX w/2.5E-4, PX_b:PX w/4.5E-4, PX_c:PX w/6.0E-4 N_E_a:Noah Eta w/2.5E-4, N_E_b:Noah Eta w/4.5E-4, N_E_c:Noah Eta w/6.0E-4 N_M_a:Noah MRF w/2.5E-4, N_M_b:Noah MRF w/4.5E-4, N_M_c:Noah MRF w/6.0E-4

21 Continued. ValleyMountain PX_a:PX w/2.5E-4, PX_b:PX w/4.5E-4, PX_c:PX w/6.0E-4 N_E_a:Noah Eta w/2.5E-4, N_E_b:Noah Eta w/4.5E-4, N_E_c:Noah Eta w/6.0E-4 N_M_a:Noah MRF w/2.5E-4, N_M_b:Noah MRF w/4.5E-4, N_M_c:Noah MRF w/6.0E-4

22 Sensitivity to Analysis Nudging Spatial Distribution of Max 8-hr O 3 Daily Max 8-hr (ppb) MAX DIFF MIN DIFF Daily Max 8-hr (ppb) MAX DIFF MIN DIFF

23 Sensitivity to Analysis Nudging Spatial Distribution of Max 8-hr O 3 Daily Max 8-hr (ppb) MAX DIFF MIN DIFF Daily Max 8-hr (ppb) MAX DIFF MIN DIFF

24 Continued. Daily Max 8-hr (ppb) MAX DIFF MIN DIFF Daily Max 8-hr (ppb) MAX DIFF MIN DIFF

25 Sensitivity to Analysis Nudging Statistics for Max 8-hr O3 Noah-Eta w/ 6.0E -4 /sec PX_a:PX w/2.5E-4, PX_b:PX w/4.5E-4, PX_c:PX w/6.0E-4 N_E_a:Noah Eta w/2.5E-4, N_E_b:Noah Eta w/4.5E-4, N_E_c:Noah Eta w/6.0E-4 N_M_a:Noah MRF w/2.5E-4, N_M_b:Noah MRF w/4.5E-4, N_M_c:Noah MRF w/6.0E-4

26 Conclusions Generally, INTERPPX gives slightly better model performance for meteorology and O3 simulation. PX model performs well for temperature at most sites but wind speed. NOAH_Eta scheme performs well for wind speed at mountain area but NOAH_MRF scheme performs well for wind speed at valley site. Statistically, NOAH_Eta with Nudging 6.0x10 -4 /sec scheme shows better model performance at mountain area due to the wind speed, NOAH_MRF with Nudging 2.5x10 -4 /sec scheme shows better model performance at valley site. Applying for analysis nudging in MM5 gives better wind speed resulting in good model performance in complex terrain at a fine grid (4-km) resolution. Wind speed is a key parameter to predict better max 8-hr O3 for SIPs at a fine grid resolution. Overall, NOAH LSM Model shows better model performance at a fine (4km) grid resolution in the complex terrain. Using 4-km grid resolution for SIPs might be desirable than 12-km grid resolution.

27 Acknowledgements Observed Data for Great Smoky Mountain National Park:Observed Data for Great Smoky Mountain National Park: Jim Renfro, Air Quality Program Manager Great Smoky Mountains National Park Resource Management & Science Division Obtained Data for ICs and BCs and Meteorological Data for VISTAS 12- km:Obtained Data for ICs and BCs and Meteorological Data for VISTAS 12- km: VISTAS (Visibility Improvement State and Tribal Association of the Southeast) Funding:Funding: TDEC (Tennessee Department of Environment and Conservation)