Attempts at using Bayesian neural networksfor uncertainty assessments of temperature forecasts
This thesis describes attempts at estimating the uncertainty of the 2-metre temperature forecast error from a probabilistic point of view, utilizing Bayesian neural networks. Bayesian neural networks are a type of machine-learning algorithms used to find patterns in data and make probabilistic predictions. Multiple fields of output data from the ECMWF IFS global model, along with temperature measu
