Webinar: Anemoi - a framework for building the AIFS and other data-driven weather forecasting models

Virtual | 30 April 2024

Presenter

Matthew Chantry, ECMWF

Description

Last year ECMWF introduced the AIFS, building on a series of innovative papers which demonstrated the potential of machine learning in creating data-driven weather forecasting models.

Machine learning models typically build on open-source frameworks, such as Tensorflow, PyTorch and JAX which allow communities to work together and build on one another's work. Weather forecasting brings its own challenges for machine learning, with large datasets and spatial-temporal problems.

In this webinar, we will introduce Anemoi, the toolbox from which the AIFS is trained. 

We have been developing this toolbox with a view to many users both inside and outside of ECMWF to use the toolbox to train both global and local weather forecasting models. The toolbox aims to allow users to bring their own datasets and customise their ML models.

We will introduce the toolbox, talk through the steps needed to train a data-driven model and explain how Anemoi helps with these tasks. Finally we will outline a roadmap for future Anemoi development.

Attendance

Please note that this webinar is open to employees of ECMWF Member and Co-operating States' Meteorological services

If you would like to attend the webinar, please complete the registration form in the left-hand side menu. We will alert you of your acceptance to the webinar and send you all the joining information and Microsoft Teams meeting details in due course. A recording will be available for those unable to attend the event.

The webinar will be conducted in English.

30 April 09:00 - 10:00 BST


Format: Online only


Registration for this webinar is closed.