My PhD project aims at enhancing our understanding of weather prediction in case of multi-modality by combining machine learning techniques with topological data analysis approaches as well as appropriate visualization tools.
To produce weather forecasts, an atmospheric model is run multiple times with slightly perturbed initial conditions and/or varied parameters resulting in an ensemble dataset. Then, the communicated forecast is based on an analysis of this ensemble -- usually using the ensemble mean as the expected value and the standard deviation as a measure of uncertainty. In most cases this analysis works well but it yields misleading results and discards crucial information in case of multi-modality in the ensemble, i.e distinct likely outcomes. Our objective is to account for multi-modality in weather prediction in order to obtain a more accurate yet simplified representation of the ensemble.
My educational background originally includes computer science and mathematics but our multi-disciplinary project allows me to learn a lot about other domains as well such as meteorology and visualization.
- 2019. Impact of sparse profile sampling on the reconstruction of subsurface ocean temperature from surface information. . 5 sider.