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Yunhee Kim, Joshua S. Fu, and Terry L. Miller University of Tennessee, Knoxville

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. Outline. Background and Objective

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Yunhee Kim, Joshua S. Fu, and Terry L. Miller University of Tennessee, Knoxville

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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 Objective • Model Configurations and Descriptions • Sensitivity to INTERPPX • Sensitivity to PBL Schemes and Analysis Nudging • Conclusions

  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, 20081. (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.3 • 1. US EPA, 2007 2. Cohan et al 2006; Zhang et al., 2006a,b; Wu et al., 2008 3. 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-km • Vertical Grid Resolution: 34 layers • Simulation Period: May 15– September 15, 2002 • MM5 (v.3.7) Options: • 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 CON US 36-km • 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 ETN 4-km VISTAS 12-km

  9. Simulation Descriptions • Descriptions: • Emissions: 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 3D FDDA + INTERPPX • 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 & Surface FDDA + INTERPPX 3D & Surface FDDA w/o INTERPPX PX Noah_Eta Noah_MRF Analysis Nudging with 2.5, 4.5, 6.0 x10-4/sec for winds

  11. 1. Step - INTERPPX • 4-km INTERPPX Simulations • INTERPPXis 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 INTERPPX

  12. Results from INTERPPX Valley Mountain • 3D INT - 3DFDDA + INPERPPX • BDINT - 3DFDDA + Surface FDDAW/ INTERPPX • BDPX - 3DFDDA + Surface FDDA W/O INTERPPX

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

  14. Time series and Statistics for O3 BDINT performed better than BDPX So BDINT was selected • 3D INT - 3DFDDA + INPERPPX • BDINT - 3DFDDA + Surface FDDAW/ INTERPPX • BDPX - 3DFDDA + Surface FDDA W/O INTERPPX

  15. 2. Step - Sensitivity to PBL 4-km PBL Sensitivity Simulations • Baseline: PX • PBL Sensitivity: N_E, N_M Valley Mountain • PX– PX PBL + INTERPPX • N_E – Noah Eta PBL • N_M – Noah MRF PBL

  16. Sensitivity to PBLStatistics for Meteorology Valley Mountain • PX– PX PBL + INTERPPX • N_E – Noah Eta PBL • N_M – Noah MRF PBL

  17. Sensitivity to PBLSpatial & Temporal Distribution of Max 8-hr O3

  18. 2. Step -Summary • PX– PX PBL + INTERPPX • N_E – Noah Eta PBL • N_M – Noah MRF PBL • 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.

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

  20. Sensitivity to Analysis NudgingTime series and Statistics for Meteorology Valley Mountain 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. Valley Mountain 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 NudgingSpatial Distribution of Max 8-hr O3 Daily Max 8-hr (ppb) MAX DIFF MIN DIFF 20 -13 Daily Max 8-hr (ppb) MAX DIFF MIN DIFF 10 -10

  23. Sensitivity to Analysis NudgingSpatial Distribution of Max 8-hr O3 Daily Max 8-hr (ppb) MAX DIFF MIN DIFF 31 -28 Daily Max 8-hr (ppb) MAX DIFF MIN DIFF 18 -17

  24. Continued. Daily Max 8-hr (ppb) MAX DIFF MIN DIFF 21 -15 Daily Max 8-hr (ppb) MAX DIFF MIN DIFF 12 -12

  25. Sensitivity to Analysis NudgingStatistics 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: 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: VISTAS (Visibility Improvement State and Tribal Association of the Southeast) • Funding: TDEC (Tennessee Department of Environment and Conservation)

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