When

Nov 08, 2023 to Nov 10, 2023
(Europe/Berlin / UTC100)

Where

Reading, UK

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Description

The goal of this three-day Boot Camp is to provide training for the participants to use machine learning (ML) in weather and climate applications on High-Performance Computing systems (HPC). The participants will get an introduction to the scientific domains of meteorology, ML, and HPC. Afterwards, they will explore the applications developed in the MAELSTROM project that include:

  • The use of citizen observations and social media data for better weather predictions
  • The use of neural network emulators for faster forecast models
  • The post-processing of ensemble and local weather forecasts
  • The prediction of large-scale weather patterns to support energy production 

See https://www.maelstrom-eurohpc.eu/products-ml-apps for further details.

The tutors are from MAELSTROM partners (https://www.maelstrom-eurohpc.eu/consortium):

  • European Centre for Medium-Range Weather Forecasts
  • Eidgenössische Technische Hochschule Zürich (ETH Zürich)
  • 4Cast GmbH & Co. KG
  • Meteorologisk Institutt
  • E4
  • Universite de Luxembourg
  • Forschungszentrum Jülich.

The MAELSTROM Boot Camp is organised back-to-back with the MAELSTROM Dissemination Workshop (https://events.ecmwf.int/event/350/) and we suggest to consider visiting both events.

Attendance

The target participants are Master and PhD students, as well as early career scientists. Participants should have a basic background either  in meteorology or machine learning or both. Some limited knowledge of Python and standard ML frameworks (e.g. TensorFlow or PyTorch) is preferred. Ideally, you are also familiar with Jupyter Notebooks and git, though this is not required. 

There will be no registration fee, but travel and accommodations must be paid by your home institution. 

If you would like to attend the MAELSTROM Boot Camp, please submit the registration form before 4 September. Acceptance notifications will be sent by 25 September.

The MAELSTROM project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 955513. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and United Kingdom, Germany, Italy, Luxembourg, Switzerland, Norway