Applications of ML/AI to weather and climate modelling have experienced an exponential growth. Weather forecasts based on AI models trained on re-analysis data are now possible with forecasting skills comparable to those of physical numerical models.

 

However, to what extent the AI approach can be extended to longer time scales is a research topic of increasing importance. At the same time, traditional atmospheric and ocean models based on numerical solutions of PDEs have increased their resolution to a few kilometers, which makes their application on climate time scale very challenging. In order to test the validity of these methodologies on climate time scales in a statistically sound way, experiments on several multi-decadal simulations are required. In this context, intermediate-complexity climate models provide convenient 'numerical laboratories'. The goal of this workshop is to present state-of-the-art applications of AI/ML, advanced numerical methods and innovative hardware/software solutions tested on intermediate-complexity models, such as the SPEEDY global model developed at ICTP. The workshop will also review the state-of-the-art of AI development.

 

Topics: 

Speakers and Panelists include:

Dhruv Balwada, Columbia University, USA

Shruti Nath, University of Oxford, UK

Tim Palmer, University of Oxford, UK

Julien Le Sommer, University of Grenoble, France
 

 

Research abstracts: In the application form, all applicants are requested to submit a brief research abstract for a poster presentation and/or contributed talk. A limited number of abstracts will be selected for the poster session or to give a talk. Please use ICTP templates available for download here and below under 'Material'.

Grants: A limited number of grants are available to support the attendance of selected participants, with priority given to participants from developing countries. There is no registration fee.