arbn climate

Stress-test buildings and building systems for future climatic conditions


Product description

arbn climate is a service that produces synthetic weather time series for anywhere on earth using measured (historical) data and the outputs of the IPCC’s newest climate simulations. The product is based on an open-source algorithm called Indra, first demonstrated in Rastogi [1]; Rastogi and Andersen [2,3].


All you need to provide us is the coordinates of the location (latitude, longitude), a name, and whether you want the resultant synthetic time series to be modified with the outputs of the IPCC’s climate change models. If you choose the climate-change-flavoured files, you will also need to select one or more future ‘epochs’, i.e., a decade from 2020-2100. You can also select one of two IPCC emissions scenarios – Representative Concentration Pathway (RCP) 8.5 or RCP 4.5. Each of these is treated as a separate job, e.g., RCP 8.5 for the 2050s (2051-2060). If you choose the vanilla synthetic time series, i.e., without a climate-change flavor, no ‘epochs’ are required. In the future, we will also allow you to upload a custom flavour, such as the time series outputs of an “Urban Heat Island” or “urbanization” model. The tool just needs a time series of modelled future daily means – these future time series could represent any phenomena that modifies the underlying climate.


  • Synthetic weather time series for any location
  • Outputs of the IPCC’s newest climate simulations

Weather files contain one year of data on :-

  • Temperature
  • Humidity
  • Solar radiation (solar gain – visible & thermal)
  • Wind speeds

P. Rastogi, ‘On the sensitivity of buildings to climate: the interaction of weather and building envelopes in determining future building energy consumption’, PhD, Ecole polytechnique fédérale de Lausanne, Lausanne, Switzerland, 2016.
P. Rastogi and M. Andersen, ‘Incorporating Climate Change Predictions in the Analysis of Weather-Based Uncertainty’, in Proceedings of SimBuild 2016, Salt Lake City, UT, USA, 2016.
P. Rastogi and M. Andersen, ‘Embedding Stochasticity in Building Simulation Through Synthetic Weather Files’, in Proceedings of BS 2015, Hyderabad, India, 2015.