Implications of employing detailed urban canopy parameters for mesoscale climate modelling: a comparison between WUDAPT and GIS databases over Vienna, Austria

Abstract

ABSTRACT:One of the major obstacles to using numerical weather prediction models for guidance on mitigating urbanization's impact on local and regional climate is the lack of detailed and model ready morphological data at urban scale. The World Urban Database and Access Portal Tool (WUDAPT) is a recent project developed to extract climate relevant information on urban areas, in the form of local climate zones (LCZs), out of remote sensing imagery. This description of the urban landscape has been tested and used for parameterization of different urban canopy models (UCM) for mesoscale studies. As detailed information is usually bounded within cities' centres, crowdsourced and remote sensing data offer the possibility to move beyond the old barriers of urban climate investigations by studying the full range of variation from the urban core to the periphery and its related impacts on local climate. Thus, for this study we sought to compare the relative impact of using the WUDAPT methodology versus a simplified definition of the urban morphology extracted out of detailed GIS information to initialize a regional weather model and compare the output against official and crowdsourced weather station networks. A case study over Vienna, Austria was conducted using the weather research forecasting (WRF) model, coupled with the building effect parameterization and building energy models (BEP-BEM) in five distinct seasonal periods. Results demonstrated that using detailed GIS data to derive morphological descriptions of LCZs for mesoscale studies provided only a marginal overall improvement over using the default WUDAPT parameters based on the ranges proposed by Stewart and Oke (2012). The findings also highlighted the importance of developing techniques that are better at capturing the morphological heterogeneity across the entire urban landscape and thus improve our understandings of UCM performance over urban areas.

High‐Resolution, Multilayer Modeling of Singapore's Urban Climate Incorporating Local Climate Zones

“…This feature is especially important for a high‐resolution study of such a city with many tall buildings as Singapore. The recent contributions by Salamanca and Martilli (), Brousse et al (), and Hammerberg et al () using MLCUM (BEP/BEM) in WRF have aided in‐depth evaluation of the UHI phenomenon in European cities.…”

“…A WUDAPT committee verifies the quality of the data and whether it accurately depicts the urban landscape (Ching et al, ). The WUDAPT methodology incorporating L0 data in WRF has introduced a standardized scientific way to describe the land cover and its thermal performance on the UHI in many studies (Brousse et al, ; Cai et al, ; Hammerberg et al, ; Ren et al, ; R. Wang et al, ).…”

The Human Influence Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones Using a Supervised Classification

“…At the 10th International Conference on Urban Climate in 2018, it became clear that LCZ maps are now regarded as a global reference for urban land cover descriptions [10]. Since LCZ maps are intended for and already used in a range of different applications, such as climate models at various scales [11][12][13][14][15][16][17][18][19][20], land use change investigations [6], or the characterization of several hundreds of crowd-sourced citizen-weather stations in terms of their local-scale surroundings [21], there is a clear need for highly accurate classification results [22]. However, it is unclear what determines the final quality of a LCZ map derived with the WUDAPT level-0 methodology.…”

Urban weather modeling applications: A Vienna case study

“…The WRF model requires a large number of input data to initialize the model. First, a land cover map was generated using the World Urban Database and Access Portal Tool (WUDAPT) methodology (Hammerberg et al 2017(Hammerberg et al , 2018 and the Local Climate Zone (LCZ) concept developed by Stewart and Oke (2012). Random Forest Classification algorithm (Bechtel et al 2015) was deployed to classify the Landsat satellite imagery to best represent each LCZ class.…”

“…The respective urban canopy parameters representing each LCZ class are discussed in detail in Stewart and Oke (2012). More detailed description depicting the step-by-step land cover mapping procedure is provided in Hammerberg et al (2018). The operational building parameters, such as the occupancy schedules and thermal properties of the building envelope, were left as default values.…”