Improving weather forecasts and reducing their computational effort to save costs and energy – these are the goals of the ASPIRE project. Meteorologist Dr. Julian Quinting from the Karlsruhe Institute of Technology (KIT) uses recurring signals in the tropical Pacific which have an important influence on atmospheric circulation in Europe. Additionally, he develops machine learning models to mimic the effects of high resolution. For his project, the young scientist now receives a start-up grant from the European Research Council.
In light of the energy crisis and climate change, the importance of reliable weather forecasts for a period between two weeks and two months is increasing. Knowing what the temperatures will be four weeks from now can be crucial when estimating a building’s heating needs and filling gas storages. Accurately predicting extreme weather events like heat waves, droughts or floods, on the other hand, helps authorities and the public prepare in time, avoiding or reducing potential damage. Dr. Julian Quinting of the KIT Weather and Climate Research Institute – Department of Troposphere Research is working on forecasts in this period, called sub-seasonal forecasts, in his new project ASPIRE (which stands for: Advance sub-seasonal predictions with reduced computational effort). The European Research Council (ERC) is supporting the project with a start-up grant.
Recurring patterns with high predictability
The excellent young scientist and his future working group aim not only to improve the accuracy of predictions, but also to reduce the computational effort, which would save costs and energy and, consequently, also reduce greenhouse gas emissions. “My underlying idea is to make more use of atmospheric system sources with high intrinsic predictability,” says Quinting. “These sources are for example recurring patterns in the atmosphere that vary on a time scale of two weeks to two months.” The meteorologist believes that the recurring signals in the tropical Pacific are particularly promising because they have a major influence on atmospheric circulation in Europe. These tropical signals are, however, insufficiently represented in numerical weather prediction models and thus prevent the full exploitation of the underlying intrinsic predictability. In ASPIRE, Quinting plans to improve the representation of tropical signals by using high spatial resolution in the tropics. However, such a high resolution usually requires more computing power. To avoid this, Quinting and his workgroup are also developing machine learning models that mimic the effects of high resolution, helping to reduce computational effort.
New possibilities for meteorological services
“With ASPIRE, we want to showcase the potential of simulations with locally high spatial resolution,” says Quinting. “Ideally, weather services will be able to make even better use of existing computing power.” If the chosen approach proves successful, it could be used for climate research on other components of the atmospheric system that also have high intrinsic predictability but are incorrectly represented in weather forecasting models.
ERC Starter Grant 2022
The ERC funds excellent young scientists who wish to start an independent career and create a working group with start-up grants. Each selected project receives up to €1.5 million per year for a maximum of five years. Under certain circumstances, up to an additional €1 million may be requested, for example for equipment or access to infrastructure. In the 2022 round of calls, the European Research Council awarded starting grants to 408 scientists based in 26 European countries, including 81 based in Germany. ERC seed grants represent a total funding volume of €636 million. 2932 requests were entered, resulting in an approval rate of 13.9%.
More information on the ERC
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