Norwegian version

Public defence: Siri Sofie Eide

Siri Sofie Eide will defend her thesis “Exploring Complexity in Meteorological Data: Enhancing Weather Forecasts Through Deep Learning-Based Post-Processing” for the PhD in Engineering Science.

The trial lecture and public defence will also be streamed live:

Ordinary opponents: 

Leader of the public defence: Associate Professor Boning Feng, OsloMet

Main supervisor: Professor Michael Riegler, OsloMet/SimulaMet

Co-supervisors: 

Abstract

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.

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