1 / 50

Inter-Comparison of CMAQ, REMSAD Model Results with IMPROVE Datasets

Inter-Comparison of CMAQ, REMSAD Model Results with IMPROVE Datasets. Gail Tonnesen, Zion Wang, Chao-Jung Chien, Bo Wang, Mohammad Omary, University of California at Riverside Bourns College of Engineering Center for Environmental Research and Technology.

sanura
Download Presentation

Inter-Comparison of CMAQ, REMSAD Model Results with IMPROVE Datasets

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Inter-Comparison of CMAQ, REMSAD Model Results with IMPROVE Datasets Gail Tonnesen, Zion Wang, Chao-Jung Chien, Bo Wang, Mohammad Omary, University of California at Riverside Bourns College of Engineering Center for Environmental Research and Technology February 12, 2002 WESTAR Annual Technical Meeting, Riverside, CA

  2. Inter-Comparison Overview • CMAQ vs. REMSAD • IMPROVE database: • Raw datasets • Reconstructed (Recon) datasets • Analysis: • Yearly comparison • Seasonal comparison • Regional comparison • Comparison at two IMPROVE sites University of California at Riverside, Regional Modeling Center (RMC)

  3. CMAQ Simulation • Domain: • 85 columns, 95 rows, 18 layers, 36km grid cells horizontally • 68 variables, • Lambert-Conformal Projection • Meteorology: • From MM5 1996 simulation • Emissions: • Mobile: version D • Area, Point, Biogenics: version E • Chemistry: CB-IV with extensions • SO2 oxidation into sulfate aerosol • VOC oxidation into secondary organic aerosol University of California at Riverside, Regional Modeling Center (RMC)

  4. REMSAD Simulation • Version: REMSAD v. 6.01 • Domain: • 120 columns, 84 rows, 12 layers • 27 species • Lat-lon coordinate • Meteorology: • From MM5 1996 simulation • Emissions: • version: E • IC/BC: • clean conditions • Chemistry: Micro CB-IV University of California at Riverside, Regional Modeling Center (RMC)

  5. Modeling Domains CMAQ REMSAD University of California at Riverside, Regional Modeling Center (RMC)

  6. IMPROVE Stations University of California at Riverside, Regional Modeling Center (RMC)

  7. IMPROVE Data • Raw dataset: • Year 1996, 104 days with available ambient data • Raw: 48-53 stations • Recon: 32 stations • Reconstructed mass species: • Sulfates (SO4), as ammonium sulfates; • Nitrates (NO3), as ammonium nitrates; • Fine soil (SOIL), sum of several inorganic elements; • Fine mass (FM), as measured PM25; • Reconstructed fine mass (RCFM): sum of (SO4+NO3+LAC+1.4*OC+SOIL) • Coarse mass (CM): PM10-PM25 • MT: as PM10 University of California at Riverside, Regional Modeling Center (RMC)

  8. Species Mapping -CMAQ/REMSAD vs. IMPROVE (Raw) University of California at Riverside, Regional Modeling Center (RMC)

  9. Species Mapping -CMAQ/REMSAD vs. IMPROVE (Recon) University of California at Riverside, Regional Modeling Center (RMC)

  10. CMAQ/REMSAD vs. IMPROVE • Analysis procedures: • Compute daily averaged level-one concentration from CMAQ & REMSAD • Extract species information & concentration from IMPROVE datasets • Identify monitoring sites within CMAQ/REMSAD domains (convert lat/lon into grid cell) • Match model predictions with IMPROVE datasets (Raw & Recon) • Generate scatter plots and time-series plots of model results vs. IMPROVE datasets. University of California at Riverside, Regional Modeling Center (RMC)

  11. Preliminary results • Scatter plots (annual & seasonal) • AllSite_AllDay • AllSite_OneDay • AllDay_OneSite • Time series data • Statistical analysis • Regression (R2) • Mean normalized bias (MNB) and error (MNE) University of California at Riverside, Regional Modeling Center (RMC)

  12. Scatter plot – all year & all stations SO4 NO3 University of California at Riverside, Regional Modeling Center (RMC)

  13. Scatter plot – all year & all stations EC OC University of California at Riverside, Regional Modeling Center (RMC)

  14. Scatter plot – all year & all stations CM SOIL University of California at Riverside, Regional Modeling Center (RMC)

  15. Scatter plot – all year & all stations PM25 RCFM University of California at Riverside, Regional Modeling Center (RMC)

  16. Scatter plot – all year & all stations PM10 Bext University of California at Riverside, Regional Modeling Center (RMC)

  17. Scatter Plot – SO4 – four seasons & all stations W Sp S F University of California at Riverside, Regional Modeling Center (RMC)

  18. Scatter Plot – NO3 – four seasons & all stations W Sp S F University of California at Riverside, Regional Modeling Center (RMC)

  19. Scatter Plot – Bext – four seasons & all stations W Sp S F University of California at Riverside, Regional Modeling Center (RMC)

  20. University of California at Riverside, Regional Modeling Center (RMC)

  21. University of California at Riverside, Regional Modeling Center (RMC)

  22. Yearly comparison University of California at Riverside, Regional Modeling Center (RMC)

  23. Yearly comparison University of California at Riverside, Regional Modeling Center (RMC)

  24. Yearly comparison of Bext(based on Raw database) University of California at Riverside, Regional Modeling Center (RMC)

  25. Yearly comparison of SO4(based on Raw database) University of California at Riverside, Regional Modeling Center (RMC)

  26. Yearly comparison of NO3(based on Raw database) University of California at Riverside, Regional Modeling Center (RMC)

  27. Yearly comparison of OC(based on Raw database) University of California at Riverside, Regional Modeling Center (RMC)

  28. Yearly comparison of EC (based on Raw database) University of California at Riverside, Regional Modeling Center (RMC)

  29. Yearly comparison of NO3(based on Raw database) University of California at Riverside, Regional Modeling Center (RMC)

  30. Yearly comparison of OC(based on Raw database) University of California at Riverside, Regional Modeling Center (RMC)

  31. Yearly comparison of SOIL(based on Raw database) University of California at Riverside, Regional Modeling Center (RMC)

  32. Yearly comparison of PM2.5(based on Raw database) University of California at Riverside, Regional Modeling Center (RMC)

  33. Yearly comparison of RCFM(based on Raw database) University of California at Riverside, Regional Modeling Center (RMC)

  34. Yearly comparison of CM(based on Raw database) University of California at Riverside, Regional Modeling Center (RMC)

  35. Yearly comparison of PM10(based on Raw database) University of California at Riverside, Regional Modeling Center (RMC)

  36. Regional evaluation • Central Rocky Mountain (CRK) • BRID, GRSA, MOZI, ROMO, WEMI, and YELL • Colorado Plateau (CPL) • BAND, BRCA, CANY, GRCA, MEVE, and PEFO • Pacific Coastal Mountains (PCM) + Southern California (SCA) • PINN, PORE, REDW, and SAGO • Sierra Nevada (SRA) + Sierra-Humboldt (SRH) • YOSE, SEQU, CRLA, and LAVO • Sonoran Desert (SON) + West Texas (WTX) • CHIR, TONT, BIBE, and GUMO • Cascade Mountains (CAS) + Northern Great Plains (NGP) • SNPA, THIS, and GLAC University of California at Riverside, Regional Modeling Center (RMC)

  37. Regional evaluation University of California at Riverside, Regional Modeling Center (RMC)

  38. Regional evaluation University of California at Riverside, Regional Modeling Center (RMC)

  39. Annual Time-series Plots – SO4 Bryce Canyon Nat’l Park (BRCA, 49) Grand Canyon Nat’l Park (GRCA, 48)

  40. Annual Time-series Plots – NO3 Bryce Canyon Nat’l Park (BRCA, 49) Grand Canyon Nat’l Park (GRCA, 48)

  41. Annual Time-series Plots – PM2.5 Bryce Canyon Nat’l Park (BRCA, 49) Grand Canyon Nat’l Park (GRCA, 48)

  42. Annual Time-series Plots – PM10 Bryce Canyon Nat’l Park (BRCA, 49) Grand Canyon Nat’l Park (GRCA, 48)

  43. Annual Time-series Plots – OC Bryce Canyon Nat’l Park (BRCA, 49) Grand Canyon Nat’l Park (GRCA, 48)

  44. Annual Time-series Plots – EC Bryce Canyon Nat’l Park (BRCA, 49) Grand Canyon Nat’l Park (GRCA, 48)

  45. Annual Time-series Plots – SOIL Bryce Canyon Nat’l Park (BRCA, 49) Grand Canyon Nat’l Park (GRCA, 48)

  46. Annual Time-series Plots – RCFM Bryce Canyon Nat’l Park (BRCA, 49) Grand Canyon Nat’l Park (GRCA, 48)

  47. Annual Time-series Plots – CM Bryce Canyon Nat’l Park (BRCA, 49) Grand Canyon Nat’l Park (GRCA, 48)

  48. Annual Time-series Plots – Bext Bryce Canyon Nat’l Park (BRCA, 49) Grand Canyon Nat’l Park (GRCA, 48)

  49. Summary & Discussions • Need a protocol for model results comparison • R2 vs. MNB/MNE • Other methodologies • Both CMAQ & REMSAD perform better in summer than winter • Model performance varies in seasons, problems in: • chemical mechanism? • emissions inventories? • Meteorology, IC/BC? • Need further analysis based upon site specific data • Why some sites perform better than other sites? • Is it region dependent? • Is it season dependent? University of California at Riverside, Regional Modeling Center (RMC)

  50. Summary & Discussions • Uncertainties in ammonia emissions inventory • CMAQ greatly underpredicts CM (coarse mass particles) concentration. • By taking CM out of the composition, model predicts reasonable well for other components. • REMSAD performs better than CMAQ for CM concentration • Relative humidity impacts: • Prediction of Bext • Month average vs. site specific f(RH) values. University of California at Riverside, Regional Modeling Center (RMC)

More Related