Comparing Standard to Feature-Based Meteorological Model Evaluation Techniques in Bogotá, Colombia

Robert Nedbor-Gross Engineering School of Sustainable Infrastructure and Environment, University of Florida, Gainesville, Florida

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Barron H. Henderson Engineering School of Sustainable Infrastructure and Environment, University of Florida, Gainesville, Florida

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Justin R. Davis Engineering School of Sustainable Infrastructure and Environment, University of Florida, Gainesville, Florida

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Jorge E. Pachón Centro Lasallista de Investigación y Modelación Ambiental, Universidad de la Salle, Bogotá, Colombia

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Alexander Rincón Secretaría Distrital de Ambiente, Bogotá, Colombia

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Oscar J. Guerrero Secretaría Distrital de Ambiente, Bogotá, Colombia

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Freddy Grajales Secretaría Distrital de Ambiente, Bogotá, Colombia

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Abstract

Standard meteorological model performance evaluation (sMPE) can be insufficient in determining “fitness” for air quality modeling. An sMPE compares predictions of meteorological variables with community-based thresholds. Conceptually, these thresholds measure the model’s capability to represent mesoscale features that cause variability in air pollution. A method that instead examines features could provide a better estimate of fitness. This work compares measures of fitness from sMPE analysis with a feature-based MPE (fMPE). Meteorological simulations for Bogotá, Colombia, using the Weather Research and Forecasting (WRF) Model provide an ideal case study that highlights the importance of fMPE. Bogotá is particularly interesting because the complex topography presents challenges for WRF in sMPE. A cluster analysis identified four dominant meteorological features associated with air quality driven by wind patterns. The model predictions are able to pass several sMPE thresholds but show poor performance for wind direction. The base simulation can be improved with alternative surface characterization datasets for terrain, soil classification, and land use. Despite doubling the number of days with acceptable specific humidity, overall acceptability was never more than 10%. By comparison, an fMPE showed that predictions were able to reproduce the air-quality-relevant features on 38.4% of the days. The fMPE is based on features derived from an observational cluster analysis that have clear relationships with air quality, which suggests that reproducing those features will indicate better air quality model performance. An fMPE may be particularly useful for high-resolution modeling (1 km or less) when finescale variability can cause poor sMPE performance even when the general pattern that drives air pollution is well reproduced.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Robert Nedbor-Gross, rnedbor1@ufl.edu

Abstract

Standard meteorological model performance evaluation (sMPE) can be insufficient in determining “fitness” for air quality modeling. An sMPE compares predictions of meteorological variables with community-based thresholds. Conceptually, these thresholds measure the model’s capability to represent mesoscale features that cause variability in air pollution. A method that instead examines features could provide a better estimate of fitness. This work compares measures of fitness from sMPE analysis with a feature-based MPE (fMPE). Meteorological simulations for Bogotá, Colombia, using the Weather Research and Forecasting (WRF) Model provide an ideal case study that highlights the importance of fMPE. Bogotá is particularly interesting because the complex topography presents challenges for WRF in sMPE. A cluster analysis identified four dominant meteorological features associated with air quality driven by wind patterns. The model predictions are able to pass several sMPE thresholds but show poor performance for wind direction. The base simulation can be improved with alternative surface characterization datasets for terrain, soil classification, and land use. Despite doubling the number of days with acceptable specific humidity, overall acceptability was never more than 10%. By comparison, an fMPE showed that predictions were able to reproduce the air-quality-relevant features on 38.4% of the days. The fMPE is based on features derived from an observational cluster analysis that have clear relationships with air quality, which suggests that reproducing those features will indicate better air quality model performance. An fMPE may be particularly useful for high-resolution modeling (1 km or less) when finescale variability can cause poor sMPE performance even when the general pattern that drives air pollution is well reproduced.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author e-mail: Robert Nedbor-Gross, rnedbor1@ufl.edu
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  • Abrams, M., 2000: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER): Data products for the high spatial resolution imager on NASA’s Terra platform. Int. J. Remote Sens., 21, 847859, doi:10.1080/014311600210326.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arciniegas, A., C. Rodriguez, J. E. Pachon, H. Sarmiento, and L. J. Hernandez, 2006: Estudio de la morbilidad en niños menores a 5 anos por enfermedad respiratoria aguda y su relación con la concentración de partículas en una zona industrial de la ciudad de Bogotá (Study of morbidity in children younger than 5 years for acute respiratory disease and its relation to concentration of particles in an industrial area of the city of Bogotá). Acta Nova, 3, 147154.

    • Search Google Scholar
    • Export Citation
  • Austin, E., A. Zanobetti, B. Coull, J. Schwartz, D. R. Gold, and P. Koutrakis, 2015: Ozone trends and their relationship to characteristic weather patterns. J. Exposure Sci. Environ. Epidemiol., 25, 532542, doi:10.1038/jes.2014.45.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bailey, D. T., 2000: Meteorological monitoring guidance for regulatory modeling applications. Environmental Protection Agency Rep. EPA-454/R-99-005, 168 pp. [available at http://www.epa.gov/scram001/guidance/met/mmgrma.pdf.]

  • Batjes, N. H., 2009: Harmonized soil profile data for applications at global and continental scales: Updates to the WISE database. Soil Use Manage., 25, 124127, doi:10.1111/j.1475-2743.2009.00202.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bell, M. L., L. A. Cifuentes, D. L. Davis, E. Cushing, A. G. Telles, and N. Gouveia, 2011: Environmental health indicators and a case study of air pollution in Latin American cities. Environ. Res., 111, 5766, doi:10.1016/j.envres.2010.10.005.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Borge, R., V. Alexandrov, J. J. Del Vas, J. Lumbreras, and E. Rodríguez, 2008: A comprehensive sensitivity analysis of the WRF model for air quality applications over the Iberian Peninsula. Atmos. Environ., 42, 85608574, doi:10.1016/j.atmosenv.2008.08.032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Boston University, 2012: User guide for the MODIS land cover type product. Land Cover and Surface Climate Group Doc., 5 pp. [Available online at http://www.bu.edu/lcsc/files/2012/08/MCD12Q1_user_guide.pdf.]

  • Bougeault, P., and P. Lacarrère, 1989: Parameterization of orography-induced turbulence in a mesobeta-scale model. Mon. Wea. Rev., 117, 18721890, doi:10.1175/1520-0493(1989)117<1872:POOITI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carvalho, D., A. Rocha, M. Gómez-Gesteira, and C. Santos, 2012: A sensitivity study of the WRF Model in wind simulation for an area of high wind energy. Environ. Model. Software, 33, 2334, doi:10.1016/j.envsoft.2012.01.019.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chang, M., S. Fan, Q. Fan, W. Chen, Y. Zhang, Y. Wang, and X. Wang, 2014: Impact of refined land surface properties on the simulation of a heavy convective rainfall process in the Pearl River delta region, China. Asia-Pac. J. Atmos. Sci., 50, 645655, doi:10.1007/s13143-014-0052-3.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheng, W. Y., and W. J. Steenburgh, 2005: Evaluation of surface sensible weather forecasts by the WRF and the Eta Models over the western United States. Wea. Forecasting, 20, 812821, doi:10.1175/WAF885.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cohen, A. E., S. M. Cavallo, M. C. Coniglio, and H. E. Brooks, 2015: A review of planetary boundary layer parameterization schemes and their sensitivity in simulating southeastern U.S. cold season severe weather environments. Wea. Forecasting, 30, 591612, doi:10.1175/WAF-D-14-00105.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, J. M., B. K. Eder, D. Nychka, and Q. Yang, 1998: Modeling the effects of meteorology on ozone in Houston using cluster analysis and generalized additive models. Atmos. Environ., 32, 25052520, doi:10.1016/S1352-2310(98)00008-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dockery, D. W., C. A. Pope, X. Xu, J. D. Spengler, J. H. Ware, M. E. Fay, B. G. Ferris Jr., and F. E. Speizer, 1993: An association between air pollution and mortality in six US cities. N. Engl. J. Med., 329, 17531759, doi:10.1056/NEJM199312093292401.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dormann, C. F., and Coauthors, 2013: Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36, 2746, doi:10.1111/j.1600-0587.2012.07348.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 30773107, doi:10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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, 8851, doi:10.1029/2002JD003296.

    • Search Google Scholar
    • Export Citation
  • Emery, C., E. Tai, and G. Yarwood, 2001: Enhanced meteorological modeling and performance evaluation for two Texas ozone episodes. Environ Final Rep. 31984-11, 235 pp. [Available online at https://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/mm/EnhancedMetModelingAndPerformanceEvaluation.pdf.]

  • Evans, J. P., M. Ekstrom, and F. Ji, 2012: Evaluating the performance of a WRF physics ensemble over south-east Australia. Climate Dyn., 39, 12411258, doi:10.1007/s00382-011-1244-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Findell, K. L., and E. Eltahir, 2003: Atmospheric controls on soil moisture–boundary layer interactions. Part I: Framework development. J. Hydrometeor., 4, 552569, doi:10.1175/1525-7541(2003)004<0552:ACOSML>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gebhart, K. A., W. C. Malm, M. A. Rodriguez, M. G. Barna, B. A. Schichtel, K. B. Benedict, J. L. Collett, and C. M. Carrico, 2014: Meteorological and back trajectory modeling for the Rocky Mountain Atmospheric Nitrogen and Sulfur Study II. Adv. Meteor., 2014, 414015, doi:10.1155/2014/414015.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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. Meteor. Climatol., 49, 760774, doi:10.1175/2009JAMC2126.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grell, G. A., and D. Dévényi, 2002: A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett., 29, doi:10.1029/2002GL015311.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., and H.-L. Pan, 1998: Convective trigger function for a mass-flux cumulus parameterization scheme. Mon. Wea. Rev., 126, 25992620, doi:10.1175/1520-0493(1998)126<2599:CTFFAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., J. Dudhia, and S. H. Chen, 2004: A revised approach to ice microphysical processes for the bulk parameterization of clouds and precipitation. Mon. Wea. Rev., 132, 103120, doi:10.1175/1520-0493(2004)132<0103:ARATIM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, doi:10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huang, L.-S., and R. L. Smith, 1999: Meteorologically‐dependent trends in urban ozone. Environmetrics, 10, 103118, doi:10.1002/(SICI)1099-095X(199901/02)10:1<103::AID-ENV341>3.0.CO;2-D.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janjić, Z. I., 1994: The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon. Wea. Rev., 122, 927945, doi:10.1175/1520-0493(1994)122<0927:TSMECM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor. Climatol., 43, 170181, doi:10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kumar, A., and N. Rojas, 2015: Statistical downscaling of WRF-Chem model: An air quality analysis over Bogota, Colombia. Geophysical Research Abstracts, Vol. 17, Abstract EGU2015-13722. [Available online at http://meetingorganizer.copernicus.org/EGU2015/EGU2015-13722-1.pdf.]

  • Lin, Y. L., R. D. Farley, and H. D. Orville, 1983: Bulk parameterization of the snow field in a cloud model. J. Appl. Meteor. Climatol., 22, 10651092, doi:10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lu, R., and R. P. Turco, 1995: Air pollutant transport in a coastal environment—II. Three-dimensional simulations over Los Angeles basin. Atmos. Environ., 29, 14991518, doi:10.1016/1352-2310(95)00015-Q.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mann, H. B., and D. R. Whitney, 1947: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat., 18, 5060, doi:10.1214/aoms/1177730491.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mass, C. F., D. Ovens, K. Westrick, and B. A. Colle, 2002: Does increasing horizontal resolution produce more skillful forecasts? Bull. Amer. Meteor. Soc., 83, 407430, doi:10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McNally, D. E., 2009: Meteorological modeling performance guidelines to replace old standards. Proc. 10th Annual Ad-Hoc Meteorological Modelers Meeting, Boulder, CO, Environmental Protection Agency. [Available online at http://www.epa.gov/scram001/adhoc/mcnally2009.pdf.]

  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, doi:10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Monin, A. S., and A. Obukhov, 1954: Basic laws of turbulent mixing in the surface layer of the atmosphere. Contrib. Geophys. Inst. Acad. Sci. USSR, 151, 163187.

    • Search Google Scholar
    • Export Citation
  • Morrison, H., G. Thompson, and V. Tatarskii, 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one and two-moment schemes. Mon. Wea. Rev., 137, 9911007, doi:10.1175/2008MWR2556.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • NCEP, 2000: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999. National Center for Atmospheric Research Computational and Information Systems Laboratory Research Data Archive, accessed 7 February 2014, doi:10.5065/D6M043C6.

    • Crossref
    • Export Citation
  • NCEP, 2014: Meteorological Assimilation Data Ingest System. NCEP Central Operations, subset used: February 2012–October 2012, accessed March 2014. [Available online at https://madis.noaa.gov/madis_api.shtml.]

  • Nielsen-Gammon, J. W., 2001: Initial modeling of the August 2000 Houston–Galveston ozone episode. Report to the Technical Analysis Division, Texas Natural Resource Conservation Commission 19, 71 pp. [Available online at https://www.driveacleanmachine.org/assets/public/implementation/air/am/contracts/reports/mm/Init_MM5_Modeling_2000Aug.pdf.]

  • Nikolakopoulos, K. G., D. A. Vaipoulos, and G. A. Skianis, 2004: ASTER DTM vs GTOPO30 and 1/250.000 topographic maps—The case of Lefkas Island, Greece. Proc. Geoscience and Remote Sensing Symp., Anchorage, AK, Institute of Electrical and Electronics Engineers, doi:10.1109/IGARSS.2004.1370807.

    • Crossref
    • Export Citation
  • Pachón, J. E., H. Sarmiento, and T. Hoshiko, 2013: Health risk represented by inhaling polycyclic aromatic hydrocarbons (PAH) during daily commuting involving using a high traffic flow route in Bogota. Salud Pública, 15, 398407.

    • Search Google Scholar
    • Export Citation
  • Pleim, J. E., 2006: A simple, efficient solution of flux-profile relationships in the atmospheric surface layer. J. Appl. Meteor. Climatol., 45, 341347, doi:10.1175/JAM2339.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pleim, J. E., 2007: A combined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing. J. Appl. Meteor. Climatol., 46, 13831395, doi:10.1175/JAM2539.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pleim, J. E., and A. Xiu, 1995: Development and testing of a surface flux and planetary boundary layer model for application in mesoscale models. J. Appl. Meteor. Climatol., 34, 1632, doi:10.1175/1520-0450-34.1.16.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Reboredo, B., R. Arasa, and B. Codina, 2015: Evaluating sensitivity to different options and parameterizations of a coupled air quality modelling system over Bogotá, Colombia. Part I: WRF Model configuration. Open J. Air Pollut., 4, 4764, doi:10.4236/ojap.2015.42006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rincon, A., and N. Rojas, 2014: Exploratory application of CCATT-BRAMS modeling system for the metropolitan area of Bogotá: Preliminary representation of meteorology and transport. Abstracts, IV Congreso Colombiano y Conferencia Internacional de Calidad del Aire y Salud Pública, Bogotá, Colombia, CASAP Technical Committee, 323–324. [Available online at http://casap.com.co/2013/en/docs/programa_final.pdf.]

  • Rincon, A., and N. Rojas, 2015: BRAMS model for a tropical inter-andean region. Presentation slides, V Congreso Colombiano y Conferencia Internacional de Calidad del Aire y Salud Pública, Bucaramanga, Colombia, CASAP Technical Committee. [Available online at http://casap.com.co/documentos/Memorias_13_agosto/440PM_Salon5_RinconA.pdf.]

  • Rodíguez, C., A. Arciniegas, J. Pachón, H. Sarmiento, and L. J. Hernández, 2006: Relationship between acute respiratory illness and air pollution in Bogotá. Revista Investigaciones En Seguridad Social Y Salud, 8.

  • Ruiz, J. J., C. Saulo, and J. Nogues-Paegle, 2010: WRF model sensitivity to choice of parameterization over South America: Validation against surface variables. Mon. Wea. Rev., 138, 33423355, doi:10.1175/2010MWR3358.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • SDA, 2010: Plan decenal de descontaminación del aire para Bogotá. Anexo del Decreto “Por medio del cual se adopta el Plan Decenal de Descontaminación del Aire para Bogotá” (Ten-year air decontamination plan for Bogotá. Annex to the Decree “The means by which the Ten-Year Plan of Air Decontamination for Bogotá is adopted”). Secretaria de Ambiente, Bogota, Colombia, 94 pp. [Available online at http://www.alcaldiabogota.gov.co/sisjur/adminverblobawa?tabla=T_NORMA_ARCHIVO&p_NORMFIL_ID=930&f_NORMFIL_FILE=X&inputfileext=NORMFIL_FILENAME.]

  • SDA, 2013a: Red de Monitoreo de Calidad del Aire de Bogotá (Air quality monitoring network of Bogotá). Secrataria Distrital de Ambiente de Bogota, accessed 14 February 2014. [Available online at http://201.245.192.252:81.]

  • SDA, 2013b: 2012 Annual Air Quality Network report. Secretaria Distrital de Ambiente de Bogota Rep. 126PM04-PR84-M-A2-V1.0, 176 pp.

  • Skamarock, W. C., and Coauthors, 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., doi:10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Sogachev, A., G. V. Menzhulin, M. Heimann, and J. O. N. Lloyd, 2002: A simple three-dimensional canopy–planetary boundary layer simulation model for scalar concentrations and fluxes. Tellus, 54B, 784819, doi:10.1034/j.1600-0889.2002.201353.x.

    • Search Google Scholar
    • Export Citation
  • Teixeira, J. C., A. C. Carvalho, T. Luna, M. J. Carvalho, and A. Rocha, 2014: Sensitivity of the WRF Model to the lower boundary in an extreme precipitation event—Madeira island case study. Nat. Hazards Earth Syst. Sci., 14, 20092025, doi:10.5194/nhess-14-2009-2014.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tesche, T. W., D. E. McNally, C. A. Emery, and E. Tai, 2001: Evaluation of the MM5 model over the midwestern U.S. for three 8-hour oxidant episodes. Report prepared for Kansas City Ozone Technical Workgroup by Alpine Geophysics LLC and ENVIRON International Corp., 151 pp.

  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, doi:10.1175/2008MWR2387.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thompson, M. L., J. Reynolds, L. H. Cox, P. Guttorp, and P. D. Sampson, 2001: A review of statistical methods for the meteorological adjustment of tropospheric ozone. Atmos. Environ., 35, 617630, doi:10.1016/S1352-2310(00)00261-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tibshirani, R., G. Walther, and T. Hastie, 2001: Estimating the number of clusters in a data set via the gap statistic. J. Roy. Stat. Soc., 63B, 411423, doi:10.1111/1467-9868.00293.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • WHO, 2006: WHO quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide: Global update 2005. World Health Organization, 20 pp. [Available online at http://apps.who.int/iris/bitstream/10665/69477/1/WHO_SDE_PHE_OEH_06.02_eng.pdf.]

  • Yao, T., J. H. Fung, H. Ma, A. K. H. Lau, P. W. Chan, J. Z. Yu, and J. Xue, 2014: Enhancement in secondary particulate matter production due to mountain trapping. Atmos. Res., 147–148, 227236, doi:10.1016/j.atmosres.2014.05.007.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yucel, I., 2006: Effects of implementing MODIS land cover and albedo in MM5 at two contrasting U.S. regions. J. Hydrometeor., 7, 10431060, doi:10.1175/JHM536.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
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