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Spatial statistics and uncertainty quantification in remote sensing and climate science, by Dr. Emily Kang.

Monday, February 26, 2024 - 10:45 to 11:30
Aula Magna

This talk presentation will discuss two examples of uncertainty quantification in climate and environmental sciences. The first part of the talk will be on the development of new multivariate spatial statistical models to enhance climate projections by combining climate model output and observational data. Emphasis will be put on the importance of UQ for spatial and spatio-temporal processes and the need for new mathematical and statistical methods to better understand climate model outputs. The second part of the talk will introduce a statistical emulator designed for remote sensing applications. This emulator is built using dimension reduction and classic Gaussian process regression and be extended and compared to machine learning methods when it comes to constructing surrogate models.

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