Can a combination of theory and Big Data better predict extreme weather impacts?


We combine simulations based on physical theory with Big Data science to predict trends in extreme weather and its impacts, and focus on uncertainties. We are working with MeteoSwiss to develop a prototype of tools for climate services.

Portrait / project description (ongoing research project)

The science part investigates how data of unknown quality can be used to validate and calibrate climate/weather-impact models. It identifies the hurdles for such an approach to be implemented in an operational model. The philosophy part develops an uncertainty typology for decision support, to further include uncertainty in Big Data. We apply argument analysis to the predictive inferences in the scientific part. We develop prerequisites to classify impacts from extreme weather to be applied to data sets from mobile communication. The synthesis part analyses conditions for transferring results to other fields and consequences for the scientific methodology and understanding.


Contrary to popular belief, Big Data science is not free of theory. But philosophical research on how it uses theories is sparse. In climate and weather research, advantages and limitations of process-based vs. statistical approaches have not been explored in detail. So far, Big Data has rarely been used to test models that take into account weather, climate and societal choices.


The goals of the project are to produce:

  1. a prototype of a climate-impact model using Big Data approaches to study the potential and limitations of such methods and quantify their uncertainty in current events and trends in extreme weather and impacts;
  2. a typology of the uncertainties and underlying arguments;
  3. criteria for the transferability of the results to other scientific fields.


There is tremendous economic and societal value in accurate quantifications of weather and climate risks, but damage estimates are often done only in hindsight. Tools and services are missing that are likely technologically feasible and meet the needs of the end-users. This project contributes to better tools and a better conceptual understanding to overcome hurdles towards operational implementation. We regularly exchange ideas and results with MeteoSwiss in developing the operational side.

Original title

Combining theory with Big Data? The case of uncertainty in prediction of trends in extreme weather and impacts

Project leaders

  • Prof. Reto Knutti, Institut für Atmosphäre und Klima, ETH Zürich
  • Prof. David Bresch, Institut für Umweltentscheidungen, ETH Zürich
  • Prof. Gertrude Hirsch Hadorn, Institut für Umweltentscheidungen, ETH Zürich



Further information on this content


Prof. Reto Knutti Institut für Atmosphäre und Klima
ETH Zürich
Gebäude CHN N 12.1
Universitätstrasse 16 8092 Zürich

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