Colin Guider (UNC-CH), GMA Seminar

Title:  Data Assimilation From a Bayesian Perspective

Abstract:  The abundance of oceanic and atmospheric data presents the question of how to incorporate this data into scientific models. Data assimilation is the process by which observations are fused with these models. We consider a probabilistic approach to data assimilation, based on a repeated application of Bayes’ Theorem. This leads to the derivation of the Kalman filtering and smoothing algorithms, as well as their practical implementations. We also consider specific models in which these algorithms are applied, such as Numerical Weather Prediction.