Publications/CV

Preprints

Rasp, S., Dueben, P. Scher, S., Weyn, J., Mouatadid, S., Thuerey, N., 2020. WeatherBench: A benchmark dataset for data-driven weather forecasting. arXiv http://arxiv.org/abs/2002.00469

Rasp, S., 2019. Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: general algorithms and Lorenz 96 case study (v1. 0). Geoscientific Model Development Discussion Paper https://www.geosci-model-dev-discuss.net/gmd-2019-319/

Beucler, T., Rasp, S., Pritchard, M. and Gentine, P., 2019. Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling. arXiv https://arxiv.org/abs/1906.06622

Rasp, S., Schulz, H., Bony, S. and Stevens, B., 2019. Combining crowd-sourcing and deep learning to explore the meso-scale organization of shallow convection. arXiv https://arxiv.org/abs/1906.01906

Papers

Hirt, M., Rasp, S., Blahak, U. and Craig, G., 2019. Stochastic parameterization of processes leading to convection initiation in kilometre-scale models. Monthly Weather Review, 147 (11), 3917-3934. https://doi.org/10.1175/MWR-D-19-0060.1

Keil, C., Baur, F., Bachmann, K., Rasp, S., Schneider, L. and Barthlott, C., 2019. Relative contribution of soil moisture, boundary layer and microphysical perturbations on convective predictability in different weather regimes Quarterly Journal of the Royal Meteorological Society, 145 (724), 3102-3115. https://doi.org/10.1002/qj.3607

Rasp, S. and Lerch, S., 2018. Neural networks for post-processing ensemble weather forecasts. Monthly Weather Review, 146(11), 3885-3900. https://doi.org/10.1175/MWR-D-18-0187.1

Rasp, S., Pritchard, M. and Gentine, P., 2018. Deep learning to represent sub-grid processes in climate models. Proceedings of the National Academy of Sciences, 115(39), 9684-9689. https://doi.org/10.1073/pnas.1810286115

Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G. and Yacalis, G., 2018. Could machine learning break the convection parameterization deadlock? Geophysical Research Letters, 45,5742–5751. https://doi.org/10.1029/2018GL078202

Rasp, S., Selz, T. and Craig, G.C., 2018. Variability and clustering of mid-latitude summertime convection: Testing the Craig and Cohen (2006) theory in a convection-permitting ensemble with stochastic boundary layer perturbations. Journal of the Atmospheric Sciences, 75(2), 691-706. DOI: http://dx.doi.org/10.1175/JAS-D-17-0258.1. More information

Rasp, S., Selz, T. and Craig, G.C., 2016. Convective and Slantwise Trajectory Ascent in Convection-Permitting Simulations of Midlatitude Cyclones. Monthly Weather Review, 144(10), 3961-3976. DOI: http://dx.doi.org/10.1175/MWR-D-16-0112.1
More information


Talks and conference presentations

Invited talks

Machine Learning for Weather and Climate Modelling, Oxford, UK, September 2019
Improving Models with Machine Learning Parameterizations: Status Quo vs. Real Progress Slides

Convection parameterization: Progress and challenges, Exeter, UK, July 2019
Machine Learning: Building parameterizations and understanding clouds Slides

Workshop on Data Science and Machine Learning, AGU Fall Meeting, Washington DC, USA, December 2018
Opportunities and challenges for machine learning parameterizations

Columbia University, New York, USA, December 2018
Deep learning to represent subgrid processes in climate models

Harvard University - ClimaTea, Cambridge, MA, USA, December 2018
Deep learning to represent subgrid processes in climate models

Massachusetts Institute of Technology, Cambridge, MA, USA, December 2018
Deep learning to represent subgrid processes in climate models

Computer vision seminar, TU Munich, Munich, Germany, August 2018
Deep learning in weather and climate science

Max-Planck-Institute for Meteorology, Hamburg, Germany, July 2018
Machine learning to represent atmospheric sub-grid processes

Conferences

Understanding Clouds and Precipitation, Berlin, Germany, February 2019 Sugar, Flower, Fish or Gravel - Cloud classification from humans to machines, Talk

American Geophysical Union Fall Meeting, Washington DC, USA, December 2018
Deep learning to represent subgrid processes in climate models, Poster

Conference on Predictability and Multi-Scale Prediction of High Impact Weather, Landshut, Germany, October 2017
Variability and clustering of mid-latitude summertime convection, Talk

Future of Cumulus Parameterization Workshop, Delft, Netherlands, July 2017
Variability of Mid-Latitude Summertime Convection, Poster

COSMO/CLM/ART User Seminar, DWD Offenbach, Germany, March 2017
COSMO-KENDA experiments with a stochastic boundary layer perturbation scheme, Talk

DACH, Berlin, Germany, March 2016
Der Einfluss eines physikalischen, stochastischen Störungenschemas auf das Störungswachstum in einem konvektiv-skaligen Modell, Talk

COSMO/CLM/ART User Seminar, DWD Offenbach, Germany, March 2016
Stochastic boundary layer perturbations: Systematic impact and perturbation growth, Poster

PANDOWAE Final Symposium, Karlsruhe, Germany, May 2015
High-resolution trajectory analysis of vertical motions in different weather situations, Poster

Workshops

Waves 2 Weather Early Career Scientists, Zugspitze, Germany, September 2018
Three-day workshop on machine learning and neural netoworks in Python Link

Max-Planck-Institute for Meteorology, Hamburg, Germany, July 2018
One-day workshop on neural networks in Python

Earth System Science department, UC Irvine, April/May 2018
Workshops on machine learning in Python, Jupyter notebooks and the xarray package


Education

2019 - Current
Postdoc, TU Munich
Group: Physics-based deep learning, Prof. Nils Thuerey
Topic: Figuring out how machine learning can actually improve weather and climate prediction

Jul - Aug 2019
Visiting Researcher, Vulcan, Inc., Seattle Group: Climate modeling group led by Prof. Chris Bretherton

Feb - May 2018
Visiting Researcher, UC Irvine
Advisor: Prof. Mike Pritchard
Topic: Improving climate model physics with neural networks.

2015 - 2019
PhD in Meteorology, LMU Munich
Project A6 of the DFG Collaborative Research Project Waves to Weather
Advisor: Prof. George C. Craig
Topic: Representing forecast uncertainty using stochastic physical prarameterizations

2012 - 2015
Master of Science in Meteorology, LMU Munich
Thesis supervisors: Prof. George C. Craig, Dr. Tobias Selz
Thesis topic: High-Resolution Trajectory Analysis of Vertical Motions in Different Weather Situations

2009 - 2012
Bachelor of Science in Physical Geography, University of Hull, UK
Thesis supervisors: Dr. Stuart McLelland
Thesis topic: The Effects of Relative Submergence on the Hydrodynamics of an Open Vegetated Channel