Sensitivity of simulated urban-atmosphere interactions in Oklahoma city to urban parameterization

Larissa J. Reames, David J. Stensrud

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The world's population is increasingly concentrated in large urban areas. Many observational and modeling studies have explored how these large, population-dense cities modify local and mesoscale atmospheric phenomena. These modeling studies often use an urban canopy model to parameterize urban surfaces. However, it is unclear whether this approach is appropriate for more suburban cities, such as those found in the Great Plains. Thus, the Weather Research and Forecasting Model was run for a week over Oklahoma City, Oklahoma, and results were compared with observations. Overall, four configurations were examined. Two simulations used the Noah LSM, one with all urban areas removed (CTRL), and the other with urban areas parameterized by a modified Noah land surface model with three urban categories (LSMMOD). Additional simulations utilized a single-layer urban canopy model (SLUCM) either with default urban fraction values (SLUCM1) or with urban fractions taken from the National Land Cover Database (SLUCM2). Results from the three urban runs compared favorably to high-density temperature observations of the urban heat island. The SLUCM1 run was the most realistic, although the urban fractions applied were the least representative of Oklahoma City. All urban runs also produced a drier and deeper planetary boundary layer over the city. The prediction of near-surface winds was most problematic, with the two SLUCM runs unable to correctly reproduce reduced wind speeds over the city. The modified Noah LSM provided best overall agreement with observations and represents a reasonable option for simulating the urban effects of more-suburban cities.

Original languageEnglish (US)
Pages (from-to)1405-1430
Number of pages26
JournalJournal of Applied Meteorology and Climatology
Issue number5
StatePublished - May 1 2017

All Science Journal Classification (ASJC) codes

  • Atmospheric Science


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