Meteorological forcing data for urban outdoor thermal comfort models from a coupled convective boundary layer and surface energy balance scheme

• Published in: Urban climate 2015-03, Vol.11, p.1-23
• Main Authors: Onomura, S., Grimmond, C.S.B., Lindberg, F., Holmer, B., Thorsson, S.
• Format: Article
• Language: English
• Subjects: Boundary layer; Fysisk geografi; Meteorologi och atmosfärforskning; Meteorology and Atmospheric Sciences; Outdoor thermal comfort; Physical Geography; Surface energy balance; Urban land surface model
• ISSN: 2212-0955; 2212-0955
• DOI: 10.1016/j.uclim.2014.11.001

•A slab convective boundary layer model is coupled to two urban land surface models.•Coupled model requires fewer forcing variables to calculate air temperature and humidity.•Coupled model can simulate urban data well forced by urban or rural observations.•Simple, rapid model provides data for applications e.g. thermal comfort, air quality.•Impact of modelled urban data on thermal comfort estimation is assessed. Site-specific meteorological forcing appropriate for applications such as urban outdoor thermal comfort simulations can be obtained using a newly coupled scheme that combines a simple slab convective boundary layer (CBL) model and urban land surface model (ULSM) (here two ULSMs are considered). The former simulates daytime CBL height, air temperature and humidity, and the latter estimates urban surface energy and water balance fluxes accounting for changes in land surface cover. The coupled models are tested at a suburban site and two rural sites, one irrigated and one unirrigated grass, in Sacramento, U.S.A. All the variables modelled compare well to measurements (e.g. coefficient of determination=0.97 and root mean square error=1.5°C for air temperature). The current version is applicable to daytime conditions and needs initial state conditions for the CBL model in the appropriate range to obtain the required performance. The coupled model allows routine observations from distant sites (e.g. rural, airport) to be used to predict air temperature and relative humidity in an urban area of interest. This simple model, which can be rapidly applied, could provide urban data for applications such as air quality forecasting and building energy modelling, in addition to outdoor thermal comfort.