An urban climate neural network screening tool

Abstract

Microscale RANS (Reynolds Averaged Navier Stokes) models are able to simulate the urban climate for entire large cities with a high spatial resolution of up to 5 m horizontally. They do this using data from geographic information systems (GIS) that must be specially processed to provide the models with information about the terrain, buildings, land use, and resolved vegetation. If high-performance computers, for example from research institutions, are not available for the simulations or are beyond the financial scope, the calculation on commercially available servers can take several weeks. The calculation of a reference initial state for a city is often followed by questions regarding adaptation measures due to climate change or the influence of smaller and larger future building developments on the urban climate. These changes lead locally to a change of the urban climate but are also influenced by the urban climate itself.In order to save computational time and to comfortably give a quantitative fast initial assessment, we trained a neural network that predicts the simulation results of a RANS model (for example: air temperature at night and during the day, wind speed, cold air flow) and implemented this network in a GIS. The tool allows to calculate the impact of development projects on the urban climate in a fraction of the time required by a RANS simulation and comes close to the RANS model in terms of accuracy. It can also be used by people without in-depth knowledge of urban climate modeling and is therefore particularly suitable for use, for example, in specialized offices of administrative departments or by project developers.