Publications/CV

Peer-reviewed publications

Rasp, S., 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


Invited Talks

[UPCOMING] Convection parameterization: Progress and challenges, Exeter, UK, July 2019

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

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

Conference presentations

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


Education

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

2015 - current
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