Predict with Confidence: Application of a Bayesian-Based Assimilation Framework to Optimise Wetland CH4 Emissions from a Terrestrial Ecosystem
Uncertainty in our calculations of a system reveals the limits of our knowledge. This limitation makes model representations of the Earth system challenging and our predictions less reliable. However, despite appearing chaotic and non-linear, a system always follows a deterministic trajectory, which can often be expressed probabilistically. Building on this concept, this thesis applies data assimi
