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Boundary-Layer Meteorology

, Volume 119, Issue 2, pp 263–288 | Cite as

Application of Refined Land-Use Categories for High Resolution Mesoscale Atmospheric Modelling

  • J. S. L. Lam
  • A. K. H. LauEmail author
  • J. C. H. Fung
Article

Abstract

A comparison of two separate MM5 land-use datasets (i.e., ‘US Geological Survey (USGS)’ and ‘Pollutants in the Atmosphere and their Transport over Hong Kong (PATH)’, each with different parameter values and different spatial distributions) was performed to understand the importance of land-surface processes and land-atmosphere interactions in the evolution of mesoscale weather phenomena during a high pollution episode in Hong Kong from 28 December 1999 through 1 January 2000. Also, a series of high resolution mesoscale numerical experiments was performed to investigate the possible roles of various surface characteristics or land-use parameters in this high pollution episode. Specifically, the relative importance of six land-use parameters including the roughness length, thermal inertia, soil moisture availability, albedo, surface heat capacity and surface emissivity are studied. Results from this study suggest that the soil moisture availability is the most important controlling parameter on the flow pattern and on surface fluxes. Sensitivity tests also show that the general flow pattern is insensitive to the other five land-use parameters

Keywords

Convergence zones Land-use category Mesoscale meteorology Sea-land breezes Surface wind and air pollution 

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References

  1. Anthes R.A., Warner T.T. (1978). ‘Development of Hydro Dynamical Model Suitable for Air Pollution and Other Meso-Meteorological Studies’. Mon. Wea. Rev 106:1045–1078CrossRefGoogle Scholar
  2. Barna M., Lamb B. (2000). ‘Improving Ozone Modelling in Regions of Complex Terrain Using Observational Nudging in a Prognostic Meteorological Model’. Atmos. Environ 34:4889–4906CrossRefGoogle Scholar
  3. Carlson T.N., Boland F.E. (1978). ‘Analysis of Urban-rural Canopy Using a Surface Heat Flux/Temperature Model’. J. Appl. Meteorol 17:998–1013CrossRefGoogle Scholar
  4. Chang D.H., Jiang L., Islam S. (2000). ‘Issue of Soil Moisture Coupling in MM5: Simulation of the Diurnal Cycle over the FIFE Area’. J. Hydrometeorol 1(6):477-490CrossRefGoogle Scholar
  5. Chen F., Dudhia J. (2001a). ‘Coupling an Advanced Land Surface-hydrology Model with the Penn State-NCAR MM5 Modelling System. Part I: Model Implementation and Sensitivity’. Mon. Wea. Rev 129(4):569–585CrossRefGoogle Scholar
  6. Chen F., Dudhia J. (2001b). ‘Coupling an Advanced Land Surface-hydrology Model with the Penn State-NCAR MM5 Modelling System. Part II: Preliminary Model Validation’. Mon. Wea. Rev 129(4):587–604CrossRefGoogle Scholar
  7. Colle B.A., Olson J.B., Tongue J.S. (2003). ‘Multiseason Verification of the MM5 Part I: Comparison with the Eta Model over the Central and Eastern United States and Impact of MM5 Resolution’. Weather and Forecasting 18(3):431–457CrossRefGoogle Scholar
  8. Crawford T.M., Stensrud D.J., Mora F., Merchant J.W., Wetzel P.J. (2001). ‘Value of Incorporating Satellite-derived Land Cover Data in MM5/PLACE for Simulating Surface Temperatures’. J. Hydrometeorol. 2(5):453–468CrossRefGoogle Scholar
  9. Dosio A., Emeis S., Graziani G., Junkermann W., Levy A. (2001). ‘Assessing the Meteological Conditions of a Deep Italian Alpine Valley System by Means of a Measuring Campaign and Simulations with Two Models During a Summer Smog Episode’. Atmos. Environ 35:5441–5454CrossRefGoogle Scholar
  10. Dudhia J. (1993). ‘A Nonhydrostatic Version of the Penn State-NCAR Mesoscale Model: Validation Tests and Simulation of an Atlantic Cyclone and Cold Front’. Mon. Wea. Rev 121:1493–1513CrossRefGoogle Scholar
  11. Fung J.C.H., Lau A.K.H., Lam J.S.L., Yuan Z .B. (2005). ‘Observational and Modeling Analysis of a Severe Air Pollution Episode in Western Hong Kong’. J .Geophys. Res 110:D09105, doi: 10.1029/2004JD005105CrossRefGoogle Scholar
  12. Grell G.A. (1993). ‘Prognostic Evaluation of Assumptions Used by Cumulus Parameterizations’. Mon. Wea. Rev 121:764–787CrossRefGoogle Scholar
  13. Hong Kong Observatory: 1999, ‘The Weather of December 1999’, Available online at http://www.weather.gov.hk/wxinfo/pastwx/mws199912.htm.Google Scholar
  14. Legates D.R., McCabe G.J. (1999). ‘Evaluating the Use of “Goodness-of-Fit” Measures in Hydrologic and Hydro Climatic Model Validation’. Water Resour. Res 35(1):233–241CrossRefGoogle Scholar
  15. Liu H., Chan J.C.L. (2002). ‘Boundary Layer Dynamics Associated with a Severe Air-Pollution Episode in Hong Kong’. Atmos. Environ 36:2013–2025CrossRefGoogle Scholar
  16. Mahrt L., Ek M. (1984). ‘The Influence of Atmospheric Stability on Potential Evaporation’. J. Climate 8:2039—2057Google Scholar
  17. Noilhan J., Planton S. (1989). ‘A Simple Parameterization of Land Surface Processes for Meteorological Models’. Mon. Wea. Rev 117:536–549CrossRefGoogle Scholar
  18. Physick W.L., Noonan J.A. (2000). ‘Mesoscale Modelling with MM5 for the PATH study’. 11th Joint Conf. on Air Pollution Meteorology, Long Beach, California:American Meteorological Society.Google Scholar
  19. Pielke R.A., Dalu G., Snook J., Lee T., Kittel T. (1991). ‘Nonlinear Influence of Mesoscale Land Use on Weather and Climate’. J. Climate 4:1053–1069CrossRefGoogle Scholar
  20. Pineda N., Jorda O., Jorge J., Baldasano J.M. (2004). ‘Using NOAA AVHRR and SPOT VGT Data to Estimate Surface Parameters: Application to a Mesoscale Meteorological Model’. Int. J. Remote Sens 25(1):129–143CrossRefGoogle Scholar
  21. Pleim J.E., Xiu A.J. (2003). ‘Development of a Land Surface Model. Part II: Data Assimilation’. J. Appl. Meteorol 42(12): 1811–1822Google Scholar
  22. PSU/NCAR Mesoscale Modeling System Tutorial Class Notes and Users′ Guide (MM5 Modeling System Version 3) 2005, available online at http://www.mmm.ucar.edu/mm5/documents/tutorial-v3-notes.htmlGoogle Scholar
  23. Seaman N.L., Stauffer D.R., Lario-Gibbs A.M. (1995). ‘A Multi-Scale Four-dimensional Data Assimilation System Applied in the San Joaquin Valley during SARMAP. Part I: Modelling Design and Basic Performance Characteristics’. J. Appl. Meteorol 34:1739–1761CrossRefGoogle Scholar
  24. Stauffer D.R., Seaman N.L. (1990). ‘Use of Four-dimensional Data Assimilation in a Limited-area Mesoscale Model. Part I: Experiments with Synoptic-scale Data’. Mon. Wea. Rev 118:1250–1277CrossRefGoogle Scholar
  25. Stauffer D.R., Seaman N.L. (1991). ‘Use of Four-dimensional Data Assimilation in a Limited-Area Mesoscale Model. Part II: Effects of Data Assimilation Within the Planetary Boundary Layer’. Mon. Wea. Rev 119:734–754CrossRefGoogle Scholar
  26. Willmott C.J. (1981). ‘On the Validation of Models’. Phys. Geogr 2:184–194Google Scholar
  27. Xiu A.J., Pleim J.E. (2001). ‘Development of a Land Surface Model. Part I: Application in a Mesoscale Meteorological Model’. J. Appl. Meteorol 40(2):192–209CrossRefGoogle Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • J. S. L. Lam
    • 1
  • A. K. H. Lau
    • 1
    Email author
  • J. C. H. Fung
    • 2
  1. 1.Centre for Coastal and Atmospheric ResearchHong Kong University of Science and TechnologyClear Water BayHong Kong
  2. 2.Department of MathematicsHong Kong University of Science and TechnologyClear Water BayHong Kong

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