Statistical science for understanding climate and the Earth system

Brief description.

Studying the Earth’s climate often involves large and complex data sets collected over space and evolving over time. These data can be traditional observations made from weather stations or satellite instruments or the output from numerical (computer) simulations of the Earth’ climate system. The Earth system refers to the rich combination of physical processes in the ocean, atmosphere and land along with human activities that interact with the physical environment at regional and global scales. A basic statistical element in this area are spatial fields for a variety of variables. These can be as simple as temperature at the surface but could be more complex measurements such as vegetation types or the concentrations  air pollutants. The challenge is to determine the structure, i.e. spatial correlation, of these fields and to predict at locations that are unobserved. This class will develop the statistical methods connected to Gaussian processes with an emphasis on applying these models to climate data. Lectures will be paired with hands-on data analysis in R. There will also be the opportunity to work on a more extensive group project that tackles a substantive climate data related question.

In a broader context this school is about statistical methods to infer curves and surfaces from noisy and perhaps indirect measurements. Although we focus on geophysical applications this school will develop function fitting methods that are general such as splines and Gaussian process regression and the foundations of solving inverse problems.  The school will also introduce the students to sparse matrix methods for large linear systems and Monte Carlo methods for Bayesian computation.

Detailed schedule: will be made available later on. 

Subject to a positive participation to the program, an attendance certificate will be awarded by Università Bocconi, mentioning that the 2024 edition of the Summer School is offered in collaboration with University of Oxford and Imperial College London.