Analyzing the sensitivity of WRF's single-layer urban canopy model to parameter uncertainty using advanced Monte Carlo simulation

Zhi Hua Wang, Elie R. Bou-Zeid, Siu Kui Au, James A. Smith

Research output: Contribution to journalArticlepeer-review

84 Scopus citations


Single-layer physically based urban canopy models (UCM) have gained popularity for modeling urban- atmosphere interactions, especially the energy transport component. For a UCM to capture the physics of conductive, radiative, and turbulent advective transport of energy, it is important to provide it with an accurate parameter space, including both mesoscale meteorological forcing and microscale surface inputs. While field measurement of all input parameters to a UCM is rarely possible, understanding the model sensitivity to individual parameters is essential to determine the relative importance of parameter uncertainty for model performance. In this paper, an advanced Monte Carlo approach-namely, subset simulation-is used to quantify the impact of the uncertainty of surface input parameters on the output of an offline modified version of the Weather Research and Forecasting (WRF)-UCM. On the basis of the conditional sampling technique, the importance of surface parameters is determined in terms of their impact on critical model responses. It is found that model outputs (both critical energy fluxes and surface temperatures) are highly sensitive to uncertainties in urban geometry, whereas variations in emissivities and building interior temperatures are relatively insignificant. In addition, the sensitivity of the model to input surface parameters is also shown to be very weakly dependent on meteorological parameters. The statistical quantification of the model's sensitivity to input parameters has practical implications, such as surface parameter calibrations in UCM and guidance for urban heat island mitigation strategies.

Original languageEnglish (US)
Pages (from-to)1795-1814
Number of pages20
JournalJournal of Applied Meteorology and Climatology
Issue number9
StatePublished - Sep 2011

All Science Journal Classification (ASJC) codes

  • Atmospheric Science


  • Heat islands
  • Mesoscale models
  • Statistical techniques
  • Stochastic models
  • Urban meteorology


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