WRF‐TEB: Implementation and Evaluation of the Coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) Model

Abstract

Urban land surface processes need to be represented to inform future urban climate and building energy projections. Here, the single layer urban canopy model Town Energy Balance (TEB) is coupled to the Weather Research and Forecasting (WRF) model to create WRF‐TEB. The coupling method is described generically, implemented into software, and the code and data are released with a Singularity image to address issues of scientific reproducibility. The coupling is implemented modularly and verified by an integration test. Results show no detectable errors in the coupling. Separately, a meteorological evaluation is undertaken using observations from Toulouse, France. The latter evaluation, during an urban canopy layer heat island episode, shows reasonable ability to estimate turbulent heat flux densities and other meteorological quantities. We conclude that new model couplings should make use of integration tests as meteorological evaluations by themselves are insufficient, given that errors are difficult to attribute because of the interplay between observational errors and multiple parameterization schemes (e.g., radiation, microphysics, and boundary layer).

Machine Learning Emulation of Urban Land Surface Processes

“…Implementation details of WRF‐TEB are provided in Meyer, Schoetter, Riechert, et al. (2020). Variables used in the WRF‐UNN and WRF‐TEB coupling are similar; however, as friction velocity u ∗ data are not provided in either observations or most ULSMs (Figure S1 in Supporting Information ), it is not part of the UNN (Section 2.2) and thus ignored in WRF‐UNN.…”

“…The innermost domain with 1 km horizontal grid spacing has a 66 m vertical grid spacing close to the surface, increasing with height. As model buildings are assumed to be within the ground (Meyer, Schoetter, Riechert, et al., 2020), the flux tower sensors at 40 m agl is 33.6 m above the model surface (as mean building height = 6.4 m, Table S3 in Supporting Information ). WPS MODIS land use data (UCAR, 2019) are used as one urban class with the same urban and vegetation fractions (i.e., all grid cells) for consistency between TEB and UNN simulations.…”

“…Software and tools are archived with a Singularity (Kurtzer et al., 2017) image and deposited on Zenodo as described in the scientific reproducibility section of Meyer, Schoetter, Riechert, et al. (2020). Users wishing to download (and reproduce) the results described in this paper can download the data archive at https://doi.org/10.5281/zenodo.5142960 (Meyer, 2021) and optionally run Singularity on their local or remote systems.…”

Machine Learning Emulation of 3D Cloud Radiative Effects

“…Software, data, and tools are archived with a Singularity (Kurtzer et al., 2017) image deposited on Zenodo as described in the scientific reproducibility section of Meyer et al. (2020). Users wishing to access (and reproduce) the results can download the data archive at https://doi.org/10.5281/zenodo.5113055 (Meyer, 2021) and optionally run Singularity on their local or remote systems.…”

uDALES: large-eddy-simulation software for urban flow, dispersion, and microclimate modelling

“…In the current platform on GitHub we include: (i) cross-platform support for GNU, Intel, and Cray compilers on Windows and macOS systems, (ii) continuous integration with build and regression tests to check for compilation and simulation errors respectively (Riechert & Meyer, 2019), and (iii) several pre-and postprocessing scripts in MATLAB as well as Singularity (Kurtzer et al, 2017) scripts to address issues of scientific reproducibility (e.g. Meyer et al (2020)). Current developments include the role of urban trees and future releases may include the use of PsychroLib (Meyer & Thevenard, 2019) to improve the calculation of psychrometric properties of air, and the ability to simulate the diurnal cycle of the urban microclimate in response to solar radiation (Suter, 2018).…”