EGU General Assembly 2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Using expired weather forecasts to supply up to 10 000 years of weather data

Petr Dolezal, Srinivasan Keshav, and Emily Shuckburgh
Petr Dolezal et al.
  • AI for the research of Environmental Risk (AI4ER), Department of Computer Science and Technology, University of Cambridge, United Kingdom

When modelling possible future renewable electricity systems, a strong focus needs to be directed to the input weather variables driving any such system. Since we cannot know the exact weather in any slightly distant future, a probabilistic approach is usually chosen, modelling the system over many possible scenarios, typically all of the past recorded weather data available. However, this narrows the range of situations considered to about 40 years, placing fundamental limits on the analysis, e.g. of rare, extreme scenarios.

In our work, we explore the possibility of using past expired ensemble forecasts from the ECMWF [1] to drastically increase the number of scenarios considered to up to 10 000 years of data. These ensemble forecasts are physical models that are regularly initialized from the same slightly perturbed snapshot, but due to the chaotic nature of weather, their predictions diverge from each other. The later stages of their predictions are thus entirely independent predictions of what the weather could have been, including the correct spatial correlations. We analyze the data from the operational archive of ECMWF to assess their suitability for modelling renewable systems of the future and demonstrate how this wealth of additional weather scenarios can enable the utilization of otherwise heavily data-dependent machine learning techniques in energy modelling. 

 [1] European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Model Ensemble extended forecast

How to cite: Dolezal, P., Keshav, S., and Shuckburgh, E.: Using expired weather forecasts to supply up to 10 000 years of weather data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17356,, 2023.