This fall (Sept 23 – Oct 25), ELLIIT, Linköping University and Lund University, are organizing an ELLIIT Focus Period on Machine Learning for Climate Science at Linköping University. More information about the focus period can be found here.
The highlight of the 5-week focus period is the scientific symposium, a three-day event on October 8-10, with a strong line-up of internationally renowned invited speakers (see here). The symposium will be held at Quality Hotel Ekoxen, starting with a welcome reception on the evening of October 7. A more detailed program will be posted on the focus period webpage shortly.
You can now register for the symposium using this link, no later than September 19th. The symposium has a limited number of participants, and registration will close earlier if capacity is reached. Priority will be given to researchers connected to ELLIIT and to researchers involved in the focus period.
More information on the symposium, including a preliminary program can be found here.
Machine Learning for Climate Science
Climate change is a complex and urgent problem that requires accurate and reliable models of the Earth’s climate systems. These models can help us understand the causes and consequences of climate change, and to evaluate the effectiveness of different policies and actions. Traditionally, mechanistic models have been used for this purpose. However, the Earth’s climate systems are extremely complex and these models are inevitably oversimplifying. For many highly critical use cases, in particular involving local events such as accurately predicting flooding, heatwaves, and storm surges, existing models need to be improved. At the same time, today we have access to a plethora of data from multiple dense networks of ground-based monitoring stations as well as both passively and actively remote-sensed data from drones, aircraft, and satellites. We are therefore seeing and increasing interest in using machine learning for improving and complementing the traditional modeling approaches. Machine learning can help us discover new patterns and relationships in the data, improve the accuracy and speed of the simulations, and result in better uncertainty quantification.