Enhanced software and platform for the Town Energy Balance (TEB) model

The Town Energy Balance (TEB) model (Masson, 2000) is a physically based single layer Urban Canopy Model (UCM) to calculate the urban surface energy balance at neighborhood scale assuming a simplified canyon geometry. It includes several capabilities (Table 1) that have been extensively evaluated offline with flux observations (Lemonsu, Grimmond, & Masson, 2004; Leroyer, Mailhot, Bélair, Lemonsu, & Strachan, 2010; Masson, Grimmond, & Oke, 2002; Pigeon, Moscicki, Voogt, & Masson, 2008) and online coupled to atmospheric models such as ALARO (Gerard, Piriou, Brožková, Geleyn, & Banciu, 2009) in ALARO-TEB (Hamdi, Degrauwe, & Termonia, 2012), the Global Environmental Multiscale (GEM; Côté et al. (1998)) in GEM-TEB (Lemonsu, Belair, & Mailhot, 2009), Meso-NH (Lac et al., 2018; Lafore et al., 1998) in TEB-MesoNH (Lemonsu & Masson, 2002), the Regional Atmospheric Modeling System (RAMS; Pielke et al. (1992)) in RAMS-TEB (Freitas, Rozoff, Cotton, & Dias, 2007), the Advanced Regional Prediction System (ARPS; Xue et al., (2000)) in ARPS-TEB (Rozoff, Cotton, & Adegoke, 2003), and the Weather Research and Forecasting (WRF; Skamarock et al. (2019)) in WRF-TEB (Meyer et al., 2020). Here, we present an enhanced software and platform for the TEB model to help scientists and practitioners wishing to use the TEB model in their research as a standalone software application or as a library in their own software. This includes several features such as crossplatform support for Windows, Linux, and macOS using CMake (Kitware Inc., 2020), static and dynamic library generation for integration with other software/models, namelist-based configuration, integration with MinimalDX (Meyer & Raustad, 2019) and PsychroLib (Meyer & Thevenard, 2019) to improve the modelling of air conditioners (AC) and psychrometric calculations respectively, a thin interface used in the coupling with WRF-CMake (Riechert & Meyer, 2019), helper functions for Python for pre- and post-processing inputs and outputs files, and a tutorial in Jupyter Notebook to allow users to quickly become familiar with the general TEB modeling workflow. In the new platform we implement testing at every code commit through continuous integration (CI) and automate the generation of documentation. The project is developed as a free, open source, community-driven project on GitHub (https://github.com/teb-model/teb) to support existing and new model applications with enhanced functionality. We welcome contributions and encourage users to provide feedback, bug reports and feature requests, via GitHub’s issue system at https://github.com/teb-model/teb/issue

Influence of large offshore wind farms on North German climate

Wind farms impact the local meteorology by taking up kinetic energy from the wind field and by creating a large wake. The wake influences mean flow, turbulent fluxes and vertical mixing. In the present study, the influences of large offshore wind farms on the local summer climate are investigated by employing the mesoscale numerical model METRAS with and without wind farm scenarios. For this purpose, a parametrisation for wind turbines is implemented in METRAS. Simulations are done for a domain covering the northern part of Germany with focus on the urban summer climate of Hamburg. A statistical-dynamical downscaling is applied using a skill score to determine the required number of days to simulate the climate and the influence of large wind farms situated in the German Bight, about 100 km away from Hamburg.Depending on the weather situation, the impact of large offshore wind farms varies from nearly no influence up to cloud cover changes over land. The decrease in the wind speed is most pronounced in the local areas in and around the wind farms. Inside the wind farms, the sensible heat flux is reduced. This results in cooling of the climate summer mean for a large area in the northern part of Germany. Due to smaller momentum fluxes the latent heat flux is also reduced. Therefore, the specific humidity is lower but because of the cooling, the relative humidity has no clear signal. The changes in temperature and relative humidity are more wide spread than the decrease of wind speed. Hamburg is located in the margins of the influenced region. Even if the influences are small, the urban effects of Hamburg become more relevant than in the present and the off-shore wind farms slightly intensify the summer urban heat island

WRF‐TEB: implementation and evaluation of the coupled Weather Research and Forecasting (WRF) and Town Energy Balance (TEB) model

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 issue of scientific reproducibility is addressed by releasing all code and data with a Singularity image. 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, boundary layer)

How many days are required to represent the urban climate statistics?

Evaluating the effect of adaptation and mitigation measures is important for urban development strategies. This can be achieved using high resolution numerical models. However, they are computationally expensive, thus simulating a 30-year climate period is challenging. An approach can be to simulate only a subset of days from the 30 years. Identifying the number of days which are sufficient to represent the urban climate is the aim of this presentation. The presented statistical dynamical downscaling method is applied to simulate the urban climate of Hamburg. It utilises 30-year time series from 27 weather stations in Northern Germany and The Netherlands. For some meteorological quantities measured at these stations, the frequency distributions have been analysed. These are compared with artificial frequency distributions built with bootstrapping and a lower number of days. For comparing these distributions, a skill score following Perkins et al. (2007) is further developed, now taking into account the relationship between the quantities. The results of this statistical dynamical downscaling method indicate that the statistics of the urban climate of Hamburg can be simulated with a much lower number of days than the 30-year time series. Perkins, S. A., A. J. Pitman, N. J. Holbrook, J. McAneney (2007): Evaluation of the AR4 climate models simulated daily maximum temperature, minimum temperature and precipitation over Australia using probability density functions, Journal of climate, 20, 4356-437