Public defence: Siri Sofie Eide
Siri Sofie Eide defended her thesis “Exploring Complexity in Meteorological Data: Enhancing Weather Forecasts Through Deep Learning-Based Post-Processing” for the PhD in Engineering Science.
Siri Sofie Eide defended her thesis “Exploring Complexity in Meteorological Data: Enhancing Weather Forecasts Through Deep Learning-Based Post-Processing” for the PhD in Engineering Science.
Trial lecture: "Data assimilation approaches for numerical weather prediction. A review of classic and currently used approaches for ensemble data assimilation in numerical weather prediction."
Ordinary opponents:
Leader of the public defence: Associate Professor Boning Feng, OsloMet
Main supervisor: Professor Michael Riegler, OsloMet/SimulaMet
Co-supervisors:
This thesis seeks to improve weather forecasting, by investigating the question: How can we best exploit the potential that lies in the complex nature of meteorological data, using Deep Learning? The work explores different levels of complexity of deep neural networks, the benefits of combining heterogeneous input data in a multimodal neural network, and the value of providing ensembles of weather forecasts to a neural network, all with a view to identify the best ways of extracting the potential within the data.
Through the development of two novel deep learning model architectures, the thesis finds that the combination of data sources in a multimodal network provides great value beyond the use of a single data source; increasing the complexity of the networks does not necessarily translate into improved performance; and the use of ensemble data shows promise, and ought to be explored further in future work.