Contrary to a common view, 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. Coupling climate with impact models, integrating big data ideas for validation and calibration, and investigating implications on uncertainty has not been tried.
Goals of the project are:
1) a prototype of a climate-impact model using Big Data ideas 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
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 datasets from mobile communication. The synthesis part analyzes conditions for transferring results to other fields and consequences for the scientific methodology and understanding.
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 developing the operational side.