Spatial Superposition Method via Model Coupling for Urban Heat Island Albedo Mitigation Strategies

Humberto Silva III School for Engineering of Matter, Transport and Energy, and National Center of Excellence on SMART Innovations, Arizona State University, Tempe, Arizona

Search for other papers by Humberto Silva III in
Current site
Google Scholar
PubMed
Close
and
Jay S. Golden Division of Earth and Ocean Sciences, Nicholas School of the Environment and Pratt School of Engineering, Duke University, Durham, North Carolina

Search for other papers by Jay S. Golden in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

A spatial superposition design is presented that couples the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with the National Center of Excellence (NCE) lumped urban thermal model for application to the city of Phoenix, Arizona. This technique utilizes an approach similar to Reynolds decomposition from turbulence theory. The presented decomposition takes the NCE model prediction from a mitigated strategy as the mean temperature and the difference between the NCE and MM5 predictions without mitigation strategy as the perturbed temperature. The goal of this coupled model is to provide spatial variability when simulating mitigation strategies for the urban heat island effect, as compared with the spatially invariant lumped model. A validation analysis was performed incorporating a maximum 35% change from the baseline albedo value for the urban environment. It is shown that the coupled model differs by up to 0.39°C with comparable average surface temperature predictions from MM5. The coupled model was also used to perform analysis of three different albedo-driven spatial mitigation schemes. This resulted in the identification that having a lesser number of mitigated points on a square urban grid in Phoenix with the same average albedo leads to a greater reduction in average hourly temperature.

Corresponding author address: Humberto Silva III, National Center of Excellence on SMART Innovations, Arizona State University, P.O. Box 875402, Tempe, AZ 85287-5402. E-mail: humberto.silva@asu.edu

Abstract

A spatial superposition design is presented that couples the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) with the National Center of Excellence (NCE) lumped urban thermal model for application to the city of Phoenix, Arizona. This technique utilizes an approach similar to Reynolds decomposition from turbulence theory. The presented decomposition takes the NCE model prediction from a mitigated strategy as the mean temperature and the difference between the NCE and MM5 predictions without mitigation strategy as the perturbed temperature. The goal of this coupled model is to provide spatial variability when simulating mitigation strategies for the urban heat island effect, as compared with the spatially invariant lumped model. A validation analysis was performed incorporating a maximum 35% change from the baseline albedo value for the urban environment. It is shown that the coupled model differs by up to 0.39°C with comparable average surface temperature predictions from MM5. The coupled model was also used to perform analysis of three different albedo-driven spatial mitigation schemes. This resulted in the identification that having a lesser number of mitigated points on a square urban grid in Phoenix with the same average albedo leads to a greater reduction in average hourly temperature.

Corresponding author address: Humberto Silva III, National Center of Excellence on SMART Innovations, Arizona State University, P.O. Box 875402, Tempe, AZ 85287-5402. E-mail: humberto.silva@asu.edu
Save
  • Alapaty, K., D. T. Olerud, K. Schere, and A. F. Hanna, 1995: Sensitivity of regional oxidant model predictions to diagnostic and prognostic meteorological fields. J. Appl. Meteor., 34, 17871801.

    • Search Google Scholar
    • Export Citation
  • ASHRAE, 2004: American Society of Heating Refrigeration and Air-Conditioning Engineers Handbook of Fundamentals. McGraw-Hill, 850 pp.

  • AZMET, cited 2009: The Arizona Meteorological Network. [Available online at http://ag.arizona.edu/azmet/.]

  • Bhardwaj, R., P. Phelan, J. Golden, and K. Kaloush, 2006: An urban energy balance for the Phoenix, Arizona USA metropolitan area. Proc. 2006 Int. Mechanical Engineering Congress and Exposition, Chicago, IL, ASME, IMECE2006-15308.

  • Chen, F., and J. Dudhia, 2001: Coupling an advanced land surface–hydrology model with the Penn State–NCAR MM5 modeling system. Part II: Preliminary model validation. Mon. Wea. Rev., 129, 587604.

    • Search Google Scholar
    • Export Citation
  • Energy Information Administration, cited 2007: U.S. Energy Information Administration. [Available online at http://www.eia.doe.gov/.]

  • Golden, J. S, P. Guthrie, K. Kaloush, and R. Britter, 2005: The summertime urban heat island hysteresis lag complexity: Applying thermodynamics, urban engineering and sustainability research. J. Roy. Inst. Civ. Eng., 158, 197210.

    • Search Google Scholar
    • Export Citation
  • Grossman-Clarke, S., J. A. Zehnder, W. L. Stefanov, Y. Liu, and M. A. Zoldak, 2005: Urban modifications in a mesoscale meteorological model and the effects on near surface variables in an arid metropolitan region. J. Appl. Meteor., 44, 12811297.

    • Search Google Scholar
    • Export Citation
  • Hoffman, K., and S. Chiang, 2004: Computational Fluid Dynamics. Vol. I, Engineering Education System, 486 pp.

  • Komarov, V. S., A. V. Lavrinenko, A. V. Kreminskii, N. Y. Lomakina, Y. B. Popov, and A. I. Popova, 2007: New method of spatial extrapolation of meteorological fields on the mesoscale level using a Kalman filter algorithm for a four-dimensional dynamic-stochastic model. J. Atmos. Oceanic Technol., 24, 182193.

    • Search Google Scholar
    • Export Citation
  • Kreyszig, E., 1999: Advanced Engineering Mathematics. 8th ed. John Wiley and Sons, 1156 pp.

  • Michalakes, J., J. Dudhia, D. Gill, J. Klemp, and W. Skamarock, 1998: Design of a next-generation regional weather research and forecast model. Towards Teracomputing, W. Zwieflhofer and N. Kreitz, Eds., World Scientific, 117–124.

  • Reynolds, O., 1895: On the dynamical theory of incompressible viscous fluids and the determination of the criterion. Philos. Trans. Roy. Soc. London, 186, 123.

    • Search Google Scholar
    • Export Citation
  • Sailor, D., and L. Lu, 2004: A top-down methodology for developing diurnal and seasonal anthropogenic heating profiles for urban areas. Atmos. Environ., 38, 27372748.

    • Search Google Scholar
    • Export Citation
  • Silva, H. R., R. Bhardwaj, P. E. Phelan, J. S. Golden, and S. Grossman-Clarke, 2009: Development of a zero-dimensional mesoscale thermal model for urban climate. J. Appl. Meteor. Climatol., 48, 657668.

    • Search Google Scholar
    • Export Citation
  • Silva, H. R., P. E. Phelan, and J. S. Golden, 2010: Modeling effects of urban heat island mitigation strategies on heat-related morbidity: A case study for Phoenix, Arizona, USA. Int. J. Biometeor., 54, 1322.

    • Search Google Scholar
    • Export Citation
  • Stefanov, W. L., M. S. Ramsey, and P. R. Christensen, 2001: Monitoring urban land cover change: An expert system approach to land cover classification of semiarid to arid urban centers. Remote Sens. Environ., 77, 173185.

    • Search Google Scholar
    • Export Citation
  • Wahab, M. A., and K. S. M. Essa, 1998: Extrapolation of solar irradiation measurements. Renew. Energy, 14, 229239.

  • White, B. G., J. Paegle, W. J. Steenburgh, J. D. Horel, R. T. Swanson, L. K. Cook, D. J. Onton, and J. G. Miles, 1999: Short-term forecast validation of six models. Wea. Forecasting, 14, 84108.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 238 69 9
PDF Downloads 110 35 2