Extreme weather events caused over
casualties in the past 20 years.
times the people killed in German traffic in the same period.
times the number of casualties in the Bosnian war.
To predict just the weather in Europe, around
observations must be processed. Every day!
times the number of observations a person could read in a day (assuming 10 per minute).
Degrees of freedom are a measure for the complexity of statistics. Our high-resolution prediction model has almost
DoF in a single simulation.
times the degrees of freedom a person could update manually in one day (assuming 5 per minute).
What data scientist do can be pretty abstract to non-datanerds. We felt we would like to give everybody some feeling for how important it is to understand and predict weather and climate better, but also how difficult it is. Finally, however, what awesome possibilities a combination of high performance computing and machine learning put in our hands! Have a look and tell us what you think!
Hello machine learning and weather & climate scientists! Learn about the first versions of our machine learning tools and solutions, including architectures and loss functions. Get the results of a survey of customized machine learning solutions here.
We're excited to have reached a big milestone end of August: a series of datasets went public.
Dataset for energy production forecast
Dataset for 2m temperature downscaling
Dataset for ensemble predictions
Datasets for 2m temp. and precipitation short-range forecasts
Dataset to emulate radiation
Feedback and comments will be much appreciated.
Why is project MAELSTROM urgently needed, and how will it impact weather & climate prediction, machine learning and supercomputing? Read in ECMWF's science blog how MAELSTROM will radically change W&C predition with new ML tools, HPC-ready development platforms for data scientists and high-performance compute system designs: ECMWF Science Blog
A paper on physics emulation using machine learning has been accepted for publication in the Journal of Advances in Modelling of the Earth System (JAMES). This work creates a MAELSTROM dataset to emulate a piece of ECMWF’s weather forecast model and will feature in JAMES as part of a new special collection on using machine learning for earth system modelling. The work emulates the parametrisation of non-orographic gravity wave drag (NOGWD), a key physical process in the accurate modelling of stratospheric winds and temperatures. Using neural networks we accurately emulate this physical process even over long simulation periods and by using a high-fidelity reference scheme, we are able to improve prediction quality when compared to the standard scheme. Creating and publishing a dataset will enable other researchers to test machine learning methods and seek to produce even better emulators. More information