Will AI change weather forecasting?

AI has the potential to speed up weather forecasting. Artificial intelligence can also make it possible to integrate weather impacts, such as energy production or the amount of storm damage, into the weather forecast in a completely new way. Weather observations and weather models capable of local accuracy, which are already in operational use by the FMI and other national meteorological services, are likely to play a key role in producing new training data. In addition, the enormous speed of data-driven forecasting methods attracts research into how the uncertainty of the forecast could be assessed more cost-effectively, using data-driven methods compared to the current ensemble forecasting technology.

However, AI models and their quality do not happen in a vacuum; they are only as good as the data used for training the methods. Re-analysis data and the underlying weather models must continue to be developed to improve the quality of data-driven forecasting methods. Creating and updating data, as well as training AI forecasting methods, are all computationally highly intensive activities that will continue to require supercomputer capacity.

It is clear that the new possibilities offered by AI will also change weather forecasting, but the possibilities and limitations of the methods and data must be understood. The pace of development of artificial intelligence methods and their application is tremendous right now, so it is challenging to assess how great the ultimate transformation will be.