Simulating smoke transport from wildland fires with a regional-scale air quality model: Sensitivity to uncertain wind fields

Journal of Geophysical Research Atmospheres

Volumn 118, Issue 12, 2013, Pages 6493-6504

  Garcia Menendez, Fernando ^a   Hu, Yongtao ^a   Odman, Mehmet Talat ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

CMAQ; fire emissions; PM2.5; wind; WRF

Indexed keywords

AIR QUALITY; FIRES; METEOROLOGY; PARTICLES (PARTICULATE MATTER); UNCERTAINTY ANALYSIS; URBAN GROWTH; WIND; WIND EFFECTS;

CHEMICAL TRANSPORT MODELS; CMAQ; COMMUNITY MULTI-SCALE AIR QUALITIES; FIRE EMISSIONS; PM2.5; WEATHER RESEARCH AND FORECASTING MODELS; WIND SPEED AND DIRECTIONS; WRF;

WEATHER FORECASTING;

AIR QUALITY; ATMOSPHERIC TRANSPORT; BIOGENIC EMISSION; CLIMATE MODELING; CONCENTRATION (COMPOSITION); METEOROLOGY; SENSITIVITY ANALYSIS; SMOKE; UNCERTAINTY ANALYSIS; URBAN SITE; WILDFIRE; WIND FIELD; WIND VELOCITY;

EID: 84880896134     PISSN: None     EISSN: 21698996     Source Type: Journal    
DOI: 10.1002/jgrd.50524     Document Type: Article

References (41)

1. Achtemeier, G. L., S. A. Goodrick, Y. Liu, F. Garcia-Menendez, Y. Hu, and, M. T. Odman, (2011), Modeling smoke plume-rise and dispersion from southern United States prescribed burns with Daysmoke, Atmosphere, 2 (3), 358-388, doi: 10.3390/atmos2030358.
2. Appel, K. W., S. J. Roselle, R. C. Gilliam, and, J. E. Pleim, (2010), Sensitivity of the Community Multiscale Air Quality (CMAQ) model v4.7 results for the eastern United States to MM5 and WRF meteorological drivers, Geosci. Model Dev., 3 (1), 169-188, doi: 10.5194/gmd-3-169-2010.
3. Borge, R., V. Alexandrov, J. J. del Vas, J. Lumbreras, and, E. Rodriguez, (2008), A comprehensive sensitivity analysis of the WRF model for air quality applications over the Iberian Peninsula, Atmos. Environ., 42 (37), 8560-8574, doi: 10.1016/j.atmosenv.2008.08.032.
4. Chen, S. H., and, W. Y. Sun, (2002), A one-dimensional time dependent cloud model, J. Meteorol. Soc. Jpn., 80 (1), 99-118, doi: 10.2151/jmsj.80.99.
5. Chen, J., J. Vaughan, J. Avise, S. O'Neill, and, B. Lamb, (2008), Enhancement and evaluation of the AIRPACT ozone and PM2.5 forecast system for the Pacific Northwest, J. Geophys. Res., 113, D14305, doi: 10.1029/2007JD009554.
6. de Foy, B., M. Zavala, N. Bei, and, L. T. Molina, (2009), Evaluation of WRF mesoscale simulations and particle trajectory analysis for the MILAGRO field campaign, Atmos. Chem. Phys., 9 (13), 4419-4438, doi: 10.5194/acp-9-4419-2009.
7. Delfino, R. J., et al. (2009), The relationship of respiratory and cardiovascular hospital admissions to the southern California wildfires of 2003, Occup. Environ. Med., 66 (3), 189-197, doi: 10.1136/oem.2008.041376.
8. Dudhia, J., (1989), Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model, J. Atmos. Sci., 46 (20), 3077-3107, doi: 10.1175/1520-0469(1989)046<3077:nsocod>2.0. co;2.
9. Ek, M. B., K. E. Mitchell, Y. Lin, E. Rogers, P. Grunmann, V. Koren, G. Gayno, and, J. D. Tarpley, (2003), Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res., 108 (D22), 8851, doi: 10.1029/2002JD003296.
10. Emery, C., E. Tai, and, G. Yarwood, (2001), Enhanced meteorological modeling and performance evaluation for two Texas ozone episodes, report, Tex. Nat. Resour. Conserv. Comm., Novato, Calif.
11. Fox, D. G., and, A. R. Riebau, (2009), Effects of forest fires on visibility and air quality, in Wildland Fires and Air Pollution, edited by, A. Bytnerowicz, M. J. Arbaugh, A. R. Riebau, and, C. Andersen, pp. 171-195, Elsevier, Amsterdam, The Netherlands.
12. Garcia-Menendez, F., A. Yano, Y. Hu, and, M. T. Odman, (2010), An adaptive grid version of CMAQ for improving the resolution of plumes, Atmos. Pollut. Res., 1 (4), 239-249, doi: 10.5094/APR.2010.031.
13. Gilliam, R. C., and, J. E. Pleim, (2010), Performance assessment of new land surface and planetary boundary layer physics in the WRF-ARW, J. Appl. Meteorol. Climatol., 49 (4), 760-774, doi: 10.1175/2009jamc2126.1.
14. Gilliam, R. C., C. Hogrefe, and, S. T. Rao, (2006), New methods for evaluating meteorological models used in air quality applications, Atmos. Environ., 40 (26), 5073-5086, doi: 10.1016/j.atmosenv.2006.01.023.
15. Godowitch, J. M., R. C. Gilliam, and, S. T. Rao, (2011), Diagnostic evaluation of ozone production and horizontal transport in a regional photochemical air quality modeling system, Atmos. Environ., 45 (24), 3977-3987, doi: 10.1016/j.atmosenv.2011.04.062.
16. Goodrick, S. L., G. L. Achtemeier, N. K. Larkin, Y. Liu, and, T. M. Strand, (2012), Modelling smoke transport from wildland fires: A review, Int. J. Wildland Fire, 22 (1), pp. 83-94, doi: 10.1071/WF11116.
17. Grell, G. A., J. Dudhia, and, D. R. Stauffer, (1994), A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5), NCAR Tech. Note, NCAR/TN-398+STR, Mesoscale and Microscale Meteorol. Div., Natl. Cent. for Atmos. Res., Boulder, Colo.
18. Hogrefe, C., S. T. Rao, P. Kasibhatla, G. Kallos, C. J. Tremback, W. Hao, D. Olerud, A. Xiu, J. McHenry, and, K. Alapaty, (2001), Evaluating the performance of regional-scale photochemical modeling systems: Part I-meteorological predictions, Atmos. Environ., 35 (24), 4159-4174, doi: 10.1016/S1352-2310(01)00182-0.
19. Hong, S. Y., Y. Noh, and, J. Dudhia, (2006), A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134 (9), 2318-2341, doi: 10.1175/mwr3199.1.
20. Hu, Y. T., M. T. Odman, and, A. G. Russell, (2006), Mass conservation in the Community Multiscale Air Quality model, Atmos. Environ., 40 (7), 1199-1204, doi: 10.1016/j.atmosenv.2005.10.038.
21. Hu, Y. T., M. E. Chang, A. G. Russell, and, M. T. Odman, (2010), Using synoptic classification to evaluate an operational air quality forecasting system in Atlanta, Atmos. Pollut. Res., 1 (4), 280-287, doi: 10.5094/apr.2010.035.
22. Hwang, D., D. W. Byun, and, M. Talat Odman, (1997), An automatic differentiation technique for sensitivity analysis of numerical advection schemes in air quality models, Atmos. Environ., 31 (6), 879-888, doi: 10.1016/s1352-2310(96)00240-3.
23. Johnston, F. H., S. B. Henderson, Y. Chen, J. T. Randerson, M. Marlier, R. S. DeFries, P. Kinney, D. Bowman, and, M. Brauer, (2012), Estimated global mortality attributable to smoke from landscape fires, Environ. Health Perspect., 120 (5), 695-701, doi: 10.1289/ehp.1104422.
24. Junquera, V., M. M. Russell, W. Vizuete, Y. Kimura, and, D. Allen, (2005), Wildfires in eastern Texas in August and September 2000: Emissions, aircraft measurements, and impact on photochemistry, Atmos. Environ., 39 (27), 4983-4996, doi: 10.1016/j.atmosenv.2005.05.004.
25. Kain, J. S., (2004), The Kain-Fritsch convective parameterization: An update, J. Appl. Meteorol., 43 (1), 170-181, doi: 10.1175/1520-0450(2004) 043<0170:TKCPAU>2.0.CO;2.
26. Kochi, I., G. H. Donovan, P. A. Champ, and, J. B. Loomis, (2010), The economic cost of adverse health effects from wildfire-smoke exposure: A review, Int. J. Wildland Fire, 19 (7), 803-817, doi: 10.1071/wf09077.
27. Konovalov, I. B., M. Beekmann, I. N. Kuznetsova, A. Yurova, and, A. M. Zvyagintsev, (2011), Atmospheric impacts of the 2010 Russian wildfires: Integrating modelling and measurements of an extreme air pollution episode in the Moscow region, Atmos. Chem. Phys., 11 (19), 10031-10056, doi: 10.5194/acp-11-10031-2011.
28. Koo, B., G. M. Wilson, R. E. Morris, A. M. Dunker, and, G. Yarwood, (2009), Comparison of source apportionment and sensitivity analysis in a particulate matter air quality model, Environ. Sci. Technol., 43 (17), 6669-6675, doi: 10.1021/es9008129.
29. Lin, Y. L., R. D. Farley, and, H. D. Orville, (1983), Bulk parameterization of the snow field in a cloud model, J. Clim. Appl. Meteorol., 22 (6), 1065-1092, doi: 10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.
30. Liu, Y., S. Goodrick, G. Achtemeier, W. A. Jackson, J. J. Qu, and, W. Wang, (2009), Smoke incursions into urban areas: simulation of a Georgia prescribed burn, Int. J. Wildland Fire, 18 (3), 336-348, doi: 10.1071/WF08082.
31. Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and, S. A. Clough, (1997), Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res., 102 (D14), 16,663-16,682.
32. Otte, T. L., and, J. E. Pleim, (2010), The Meteorology-Chemistry Interface Processor (MCIP) for the CMAQ modeling system: updates through MCIPv3.4.1, Geosci. Model Dev., 3 (1), 243-256, doi: 10.5194/gmd-3-243-2010.
33. Rutledge, S. A., and, P. V. Hobbs, (1984), The mesoscale and microscale structure and organization of clouds and precipitation in midlatitude cyclones. XII: A diagnostic modeling study of precipitation development in narrow cold-frontal rainbands, J. Atmos. Sci., 41 (20), 2949-2972, doi: 10.1175/1520-0469(1984)041<2949:TMAMSA>2.0.CO;2.
34. Seaman, N. L., (2000), Meteorological modeling for air-quality assessments, Atmos. Environ., 34 (12-14), 2231-2259, doi: 10.1016/S1352-2310(99) 00466-5.
35. Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, M. G. Duda, X.-Y. Huang, W. Wang, and, J. G. Powers, (2008), A description of the Advanced Research WRF version 3, Tech. Note, NCAR/TN-475+STR, Natl. Cent. for Atmos. Res., Boulder, Colo.
36. Stein, A. F., G. D. Rolph, R. R. Draxler, B. Stunder, and, M. Ruminski, (2009), Verification of the NOAA Smoke Forecasting System: Model sensitivity to the injection height, Weather Forecasting, 24 (2), 379-394, doi: 10.1175/2008waf2222166.1.
37. Stohl, A., (1998), Computation, accuracy and applications of trajectories-A review and bibliography, Atmos. Environ., 32 (6), 947-966, doi: 10.1016/s1352-2310(97)00457-3.
38. Strand, T. M., N. Larkin, K. J. Craig, S. Raffuse, D. Sullivan, R. Solomon, M. Rorig, N. Wheeler, and, D. Pryden, (2012), Analyses of BlueSky Gateway PM2.5 predictions during the 2007 southern and 2008 northern California fires, J. Geophys. Res., 117, D17301, doi: 10.1029/2012JD017627.
39. Tian, D., Y. T. Hu, Y. H. Wang, J. W. Boylan, M. Zheng, and, A. G. Russell, (2009), Assessment of biomass burning emissions and their impacts on urban and regional PM2.5: A Georgia case study, Environ. Sci. Technol., 43 (2), 299-305, doi: 10.1021/es801827s.
40. Vautard, R., et al. (2012), Evaluation of the meteorological forcing used for the Air Quality Model Evaluation International Initiative (AQMEII) air quality simulations, Atmos. Environ., 53 (0), 15-37, doi: 10.1016/j.atmosenv. 2011.10.065.
41. Yang, E. S., S. A. Christopher, S. Kondragunta, and, X. Y. Zhang, (2011), Use of hourly Geostationary Operational Environmental Satellite (GOES) fire emissions in a Community Multiscale Air Quality (CMAQ) model for improving surface particulate matter predictions, J. Geophys. Res., 116, D04303, doi: 10.1029/2010JD014482.