1 / 35

Synergisms in the Development of the CMAQ and CAMx PM/Ozone Models

Synergisms in the Development of the CMAQ and CAMx PM/Ozone Models. Ralph E. Morris, Greg Yarwood Chris Emery, Bonyoung Koo ENVIRON International Corporation 101 Rowland Way Novato, CA Presented at CMAS Models-3 User’s Workshop October 27-29, 2003 Research Triangle Park, NC.

alaina
Download Presentation

Synergisms in the Development of the CMAQ and CAMx PM/Ozone Models

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. Synergisms in the Development of the CMAQ and CAMx PM/Ozone Models Ralph E. Morris, Greg Yarwood Chris Emery, Bonyoung Koo ENVIRON International Corporation 101 Rowland Way Novato, CA Presented at CMAS Models-3 User’s Workshop October 27-29, 2003 Research Triangle Park, NC Presents:slides/

  2. Introduction Numerous challenges in particulate matter modeling: • Multiple Components • SO4, NO3, SOA, POC, EC, Crustal, Coarse, Other • Multiple Processes • Gas-, Aqueous-. Heterogeneous-, Aerosol-Phase Chemistry • Rainout/washout, dry deposition of Gases and Particles • Advections and Diffusion • Clouds, Canopy, Terrain, etc. • Numerous Uncertainties • Chemistry (e.g., nitrate, SOA, aromatic, etc.), PM Size Distribution, Meteorology, Emissions, Measurements

  3. Introduction • CMAS Workshop Good Forum to Discuss Challenges, Approaches and Potential Solutions for Improving PM Modeling • CMAS Workshop Theme Emphasizes the Common Challenges of PM Modeling • One Atmosphere • One Community • One Model

  4. One Atmosphere

  5. One Community

  6. One Model CMAQ

  7. One Model? CMAQ MM5 RAMS WRF

  8. One Model?? CMAQ MM5 RAMS WRF SMOKE EMS EPS OPEM

  9. One Model??? CMAQ MM5 RAMS WRF SMOKE EMS EPS OPEM MOBILE NONROAD EDMS EMFAC AP42

  10. One Model???? CMAQ MM5 RAMS WRF SMOKE EMS EPS OPEM MOBILE NONROAD EDMS EMFAC AP42 IMPROVE CASTNET STN AQS/AIRS NADP SuperSites

  11. Multi-Model Intercomparisons • Intercomparing models and alternative formulations is an integral part of model development • Photochemical grid model development has taught us that much more can be learned from comparing different models with different formulations – this is even more true for PM models due to more uncertainties in processes Early 1980s UAM vs. CIT ~ 1990 UAM vs. CALGRID Early 1990s UAM-V vs. UAM vs. SAQM Mid 1990s UAM-V vs. CAMx vs. MAQSIP Early 2000s CMAQ vs. CAMx

  12. Early CMAQ vs. CAMx Comparisons for Ozone • 1991 Lake Michigan Ozone Study (LMOS) Databases • Tesche ands co-workers (2001) (available at www.crcao.com as CRC Project A-25) • MM5 and RAMS Meteorology • No one model performing sufficiently better than another • CMAQ and CAMx using MM5 more similar than CAMx using RAMS • Similar ozone responses to VOC/NOx controls • CMAQ using QSSA and SMVGEAR chemistry solvers takes ~5 and ~8 times longer to run than CAMx  EPA implements faster Hertel/MEBI chemistry solver in CMAQ

  13. Early CMAQ vs. CAMx Comparisons for Ozone • July 1995 NARSTO-Northeast Ozone Episode • Morris and co-workers (available at www.crcao.com as CRC Project A-24) • MM5 and RAMS Meteorology • Layer 1 KV mixing issues  EPA implements 1.0 m2/s minimum KV in MCIP, land use specific lower layers minimum KV used with CAMx • QSSA chemistry solver accuracy and stability issues  Hertel/MEBI solver implemented in CMAQ • Smolarkiewicz advection solver is overly diffusive.  Smolarkiewicz removed from CAMx (not in CMAQ)

  14. Early CMAQ vs. CAMx Comparisons for Ozone • July 1995 NARSTO-Northeast Ozone Episode • SAPRC97 chemistry more reactive than CB-IV  Both CMAQ and CAMx implement SAPRC99 chemistry • Different horizontal diffusion (KH) formulations in CMAQ and CAMx • CMAQ inversely and CAMx proportional to grid spacing  Area of future research and sensitivity tests (e.g., spawned BRAVO sensitivity test) • MM5 convective activity potentially can produce modeling artifacts  MM5 interface an area of continued research for CMAQ and CAMx

  15. Emerging PM Model Development Issues • Aqueous-Phase Chemistry • High pH dependency of aqueous-phase O3+SO2 reaction • Coarse and fine droplets may have different buffering and different pH effects on aqueous-phase sulfate formation • Test this effect using PMCAMx sectional PM model that incorporates CMU VSRM aqueous-phase chemistry module • October 17-19, 1995 Southern California PM episode • Two aqueous-phase chemistry modules used • CMU 1-section bulk module • CMU 2-section VSRM module

  16. Southern California Modeling Domain

  17. VSRM (Multi-Section) vs. Bulk Aqueous ChemistryPercent Increase in Sulfate (%) By second day, VRSM estimates ~15-30% more sulfate across the SoCAB with > 50% increase offshore and around Long Beach

  18. VSRM (Multi-Section) vs. Bulk Aqueous Chemistry VRSM can form significantly more sulfate than the bulk 1-section aqueous-phase chemistry module

  19. Emerging PM Model Development Issues • Conclusions on Bulk vs. Multi-Section Aqueous-Phase Chemistry Tests • Multi-section aqueous-phase chemistry module made significantly more sulfate in the Southern California test case • Due to low sulfate in Southern California, differences were not significant enough to appreciably affect sulfate model performance • Need further testing for eastern US where higher sulfate concentrations occur • Merging of CAMx4 and PMCAMx models provides platform for testing RADM and CMU 1-section bulk aqueous-phase chemistry modules against the CMU VSRM multi-section module • CMU VSR multi-section module requires ~5 times more CPU time than CMU 1-section module (Further optimization warranted)

  20. Emerging PM Model Development Issues • Aerosol Thermodynamics Gas/Particle Partitioning • Gas/Particle equilibrium usually assumed • ISORROPIA equilibrium scheme widely used • Fast and reliable • CMAQ, CAMx, URM, etc. • Equilibrium assumption may not always be correct, especially for coarse particles • PMCAMx sectional PM model includes three options for Gas/Particle partitioning: • Equilibrium (ISORROPIA) • Dynamic (MADM) • Hybrid (equilibrium for fine/dynamic for coarse particles) • Testing using October 1995 Southern California Database

  21. EQUI HYBR MADM +30% 14 16 PM2.5 SO4 PM10 SO4 12 14 12 10 10 8 Predicted concentration (mg/m3) Predicted concentration (mg/m3) 8 6 6 4 4 2 2 0 0 0 5 10 0 5 10 15 Measured concentration (ug/m ) Measured concentration (ug/m ) 150 100 PM2.5 Mass PM10 Mass 90 80 70 100 60 Predicted concentration (mg/m3) Predicted concentration (mg/m3) 50 40 50 30 20 10 0 0 0 50 100 150 0 20 40 60 80 100 Measured concentration (mg/m3) Measured concentration (mg/m3) Equilibrium vs. Dynamic vs. Hybrid

  22. EQUI HYBR MADM +30% PM2.5 NO3 50 PM10 NO3 50 40 40 30 30 Predicted concentration (mg/m3) Predicted concentration (mg/m3) 20 20 10 10 0 0 0 10 20 30 40 50 0 10 20 30 40 50 3 Measured concentration (ug/m ) 18 16 PM2.5 NH4 PM10 NH4 16 14 14 12 12 10 10 Predicted concentration (mg/m3) Predicted concentration (mg/m3) 8 8 6 6 4 4 2 2 0 0 0 5 10 15 0 5 10 15 Measured concentration (mg/m3) Measured concentration (mg/m3) Equilibrium vs. Dynamic vs. Hybrid

  23. Emerging PM Model Development Issues • Conclusions on use of equilibrium approach for gas/particle partitioning • For Southern California application: • dynamic and hybrid modules produce nearly identical results • most of the time equilibrium approach produces results very close to dynamic and hybrid approaches, but differences as high as 30% did occur • dynamic (MADM) approach requires approximately 10 times the CPU time as equilibrium approach • Further tests of equilibrium assumption warranted • Given sufficient accuracy, uncertainties and computational requirements, equilibrium approach appears adequate for annual modeling

  24. Emerging PM Model Development Issues • Particle Size Distribution • Different representations of particle size distribution in difference models • CMAQ modal approach using 3 modes and assumes all secondary PM is fine • CAMx4, REMSAD and MADRID1 assume fine and coarse PM (all secondary PM is fine) • PMCAMx, CMAQ-AIM and MADRID2 are fully sectional models where PM10 is divided up into N sections (e.g., N=10)

  25. Emerging PM Model Development Issues • Particle Size Distribution • Testing of assumptions of particle size distribution using new merged CAMx4/PMCAMX code • M4 = CAMx4 2 section plus RADM aqueous • EQUI = N sections equilibrium + VRSM aqueous • MADM = 10 sections dynamic + VRSM aqueous • RADM/EQ = 10 sections equil. + RADM aqueous • RADM/EQ4 = 4 sections equil. + RADM aqueous • October 17-18, 1995 Southern California Episode

  26. 24-Hour Sulfate (g/m3) • October 18, 1995 • M4 peak SO4 39 g/m3 • EQUI peak SO4 51 g/m3 • ~ Long Beach Area • Differences due to more sulfate production in CMU VRSM than RADM aqueous-phase chemistry • Further downwind (Riverside) M4 produces more sulfate than EQUI M4 EQUI

  27. 24-Hour Nitrate (g/m3) • October 18, 1995 • M4 peak NO3 83 g/m3 • EQUI peak NO3 54 g/m3 • Observed NO3 peak at Riverside ~40 g/m3 • Differences partly due to assuming all nitrate is fine vs. PM nitrate represented by 10 size sections (EQUI) • Differences in M4 RADM and EQU VSRM also contribute M4 EQUI

  28. M4 - EQUI • 24-Hour Nitrate (g/m3) • October 18, 1995 • M4 peak NO3 83 g/m3 • EQUI peak NO3 54 g/m3 • EQUI 10-Section grows PM NO3 into coarser sections where it dry deposits faster than M4 NO3 that is assumed to be fine • Result is less NO3 in downwind Riverside area that agrees better with observations M4

  29. Sensitivity to Number of Size Sections (10 vs. 4) @ (34,16)

  30. CPU hours per simulation day (based on Athlon 1600 CPU) 63 5.8 1.2 0.52 0.42 Computational Efficiency Model Configurations

  31. Emerging PM Model Development Issues • Nighttime Nitrate Chemistry • September 2003 CMAQ release • Zero N2O5+H2O gas-phase reaction rate • 0.02 and 0.002 probability for heterogeneous rate • April 2003 CAMx4 release • Keep gas-phase N2O5+H2O reaction rate • German smog tests provide upper bound rate, but is real gas-phase reaction • Current research suggests part of overestimation tendency may be due in part to assuming all nitrate is fine • More updates in future

  32. Emerging PM Model Development Issues • Interface with Meteorological Model (MM5/RAMS) • Mass Conservations and Mass Consistency • Clouds and Precipitation (resolved and unresolved) • Instantaneous meteorological data (convective down bursts) • MM5 PBL heights – what to do when collapsed from clouds/snow

  33. Conclusions on Model Development Synergisms • CMAQ and CAMx offer two completely different platforms to test alternative PM modules and formulations • provides an “independent” test of the assumptions • identifies potential for introducing compensatory errors • Numerous common challenges in PM modeling, the more ways of looking at the problem the better • nitrate formation, size sections and deposition • aqueous-phase chemistry • PM size distribution • meteorology • computational efficiency

  34. Toola to Facilitate Model Intercomparisons • MM5 Interface Software • MCIP 2.2 • MM5CAMx + kvpatch • CMAQ-to-CAMx conversion software • Emissions • IC/BC • CAMx-to-CMAQ conversion software • Emissions • IC/BC

  35. Current CMAQ/CAMx Comparisons • 1996 Western USA • WRAP and CRC • Jan 2002, July 2001, July 1991Eastern USA • VISTAS • August – September 1997 Southern CalEfornia • CRC • Midwest US/Supersites • MRPO

More Related