Project with Colloquium (MA8114) at TUM: Probabilistic Downscaling of Climate Variables Using Denoising Diffusion Probabilistic Models
Supervisor: Prof. Dr. Rüdiger Westermann (Chair of Computer Graphics and Visualization)
Advisor: Kevin Höhlein (Chair of Computer Graphics and Visualization)
Downscaling combines methods that are used to infer high-resolution information from low-resolution climate variables. We approach this problem as an image super-resolution task and employ Denoising Diffusion Probabilistic Model to generate finer-scale variables conditioned on coarse-scale information. Experiments are conducted on WeatherBench dataset by analysing temperature at 2 m height above the surface variable. See the final report here.
- Liangwei Jiang (2021) Image-Super-Resolution-via-Iterative-Refinement [Source code]
- Song et al. (2021) Score-Based Generative Modeling through Stochastic Differential Equations [Source code]
- Stephan Rasp, Peter D. Dueben, Sebastian Scher, Jonathan A. Weyn, Soukayna Mouatadid, and Nils Thuerey, 2020. WeatherBench: A benchmark dataset for data-driven weather forecasting. arXiv: WeatherBench: A benchmark dataset for data-driven weather forecasting