Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 549730
Title On- and off-line evaluation of the single-layer urban canopy model in London summertime conditions
Author(s) Tsiringakis, Aristofanis; Steeneveld, Gert Jan; Holtslag, Albert A.M.; Kotthaus, Simone; Grimmond, Sue
Source Quarterly Journal of the Royal Meteorological Society (2019). - ISSN 0035-9009
DOI https://doi.org/10.1002/qj.3505
Department(s) Meteorology and Air Quality
WIMEK
Publication type Refereed Article in a scientific journal
Publication year 2019
Keyword(s) boundary layer - land–atmosphere interactions - single-layer urban canopy model - surface energy balance - urban meteorology - Weather Research and Forecasting (WRF) model
Abstract

Urban canopy models are essential tools for forecasting weather and air quality in cities. However, they require many surface parameters, which are uncertain and can reduce model performance if inappropriately prescribed. Here, we evaluate the model sensitivity of the single-layer urban canopy model (SLUCM) in the Weather Research and Forecasting (WRF) model to surface parameters in two different configurations, one coupled to the overlying atmosphere (on-line) in a 1D configuration and one without coupling (off-line). A two-day summertime period in London is used as a case study, with clear skies and low wind speeds. Our sensitivity tests indicate that the SLUCM reacts differently when coupled to the atmosphere. For certain surface parameters, atmospheric feedback effects can outweigh the variations caused by surface parameter settings. Hence, in order to fully understand the model sensitivity, atmospheric feedback should be considered.

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