Short-term precipitation forecasting with deep neural nets
In this project, the overall objective will be to study, develop and test various deep neural networks on a gridded dataset of radar reflectivities with high temporal resolution. The work will naturally also include analysis of forecast skill and properties. Case studies of extreme precipitation events are also of special interest. The project can to some extent also be adapted to the student's interest.
The goal is to study, develop, and test various deep neural networks on a gridded dataset of radar reflectivities with high temporal resolution.
Deep understanding of deep neural networks
Working on a real-world application
Collaboration with researchers
Possibility to implement and research a novel approach
Knowledge about deep learning is an advantage
- Michael Riegler
- Hugo Hammer
- John Bjørnar Bremnes, Research scientist, Meteorologisk Institutt, email@example.com
- Siri Sofie Eide, Phd candidate, Meteorologisk Institutt, firstname.lastname@example.org
- Shi, X. et. al. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in neural information processing systems 28, 802-810.
- Sønderby, C. K. et. al. (2020). MetNet: A Neural Weather Model for Precipitation Forecasting, arxiv.org/abs/2003.12140