Skip to content
- A. Mozaffari, M. Langguth, B. Gong, J. Ahring, A. R. Campos, P. Nieters, O. J. Campos Escobar, M. Wittenbrink, P. Baumann, M. G. Schultz; HPC-oriented Canonical Workflows for Machine Learning Applications in Climate and Weather Prediction. Data Intelligence 2022; 4 (2): 271–285. doi: https://doi.org/10.1162/dint_a_00131
- B. Gong, M. Langguth, Y. Ji, A. Mozaffari, S. Stadtler, K. Mache, and M. G. Schultz, (preprint) Temperature forecasting by deep learning methods, Geoscientific Model Development Discussions, 2022, 1—35, https://doi.org/10.5194/gmd-2021-430
- S. Kesselheim, A. Herten, K. Krajsek, J. Ebert, J. Jitsev, M. Cherti, M. Langguth, B. Gong, S. Stadtler, A. Mozaffari, G. Cavallaro, R. Sedona, A. Schug, A. Strube, R. Kamath, M. G. Schultz, M. Riedel, T. Lippert (2021). JUWELS Booster – A Supercomputer for Large-Scale AI Research. In: Jagode, H., Anzt, H., Ltaief, H., Luszczek, P. (eds) High Performance Computing. ISC High Performance 2021. Lecture Notes in Computer Science(), vol 12761. Springer, Cham. https://doi.org/10.1007/978-3-030-90539-2_31
- M.G. Schultz, C. Betancourt, B. Gong, F. Kleinert, M. Langguth, L.H. Leufen, A. Mozaffari, S. Stadtler, “Can deep learning beat numerical weather prediction?” Philosophical Transactions of the Royal Society, Series A, 20200097, 2021. doi: 10.1098/rsta.2020.0097
- R. Glowienka-Hense, A. Hense, S. Brune, and J. Baehr, “Comparing forecast systems withmultiple correlation decomposition based on partial correlation” Advances in Statistical Climatology, Meteorology and Oceanography, 6(2):103–113, 2020.doi:10.5194/ascmo-6-103-2020.
- Sebastian Villarroy, Peter Baumann “On the Integartion of Machine Learning and Array Databases, 2020 IEEE 36th International Conference on Data Engineering (ICDE), April 2020
- Jonas Rebstadt, „Deep Hyperresolution for Weather Forecasting“, Masterarbeit, Universität Osnabrück, 16. August 2019
- Severin Hußmann, „Deep Learning for Future Frame Prediction of Weather Maps”, Masterarbeit, Humboldt-Universität Berlin, 01. August 2019
- Andreas Hense und Felix Kleinert, “DeepRain – Improving local scale rainfall prediction through deep learning”, Vortrag beim BMBF-Statustreffen im Bereich des Maschinen Lernen in Dortmund, Deutschland, 05. Juni 2019.
- Poster zum Vortrag ” DeepRain – Improving local scale rainfall prediction through deep learning” beim BMBF Statustreffen im Bereich Maschinen Lernen in Dortmund.
- Martin Schultz, “DeepRain – Improved local-scale prediction of precipitation through deep Learning”, Vortrag bei European Geophysical Union General Assembly in Wien, 11. April 2019.
- Rita Glowienka-Hense und Andreas Hense, “Partial correlation the natural correlation skill score”, Vortrag bei Meteorologentagung DACH in Garmisch-Partenkirchen, Deutschland, 18.–22. März 2019.
