Stephan Rasp
Stephan Rasp
Senior Research Scientist at Google Research
Verified email at - Homepage
Cited by
Cited by
Deep learning to represent subgrid processes in climate models
S Rasp, MS Pritchard, P Gentine
Proceedings of the national academy of sciences 115 (39), 9684-9689, 2018
Could machine learning break the convection parameterization deadlock?
P Gentine, M Pritchard, S Rasp, G Reinaudi, G Yacalis
Geophysical Research Letters 45 (11), 5742-5751, 2018
Neural networks for postprocessing ensemble weather forecasts
S Rasp, S Lerch
Monthly Weather Review 146 (11), 3885-3900, 2018
WeatherBench: a benchmark data set for data‐driven weather forecasting
S Rasp, PD Dueben, S Scher, JA Weyn, S Mouatadid, N Thuerey
Journal of Advances in Modeling Earth Systems 12 (11), e2020MS002203, 2020
Enforcing analytic constraints in neural networks emulating physical systems
T Beucler, M Pritchard, S Rasp, J Ott, P Baldi, P Gentine
Physical Review Letters 126 (9), 098302, 2021
Data‐driven medium‐range weather prediction with a resnet pretrained on climate simulations: A new model for weatherbench
S Rasp, N Thuerey
Journal of Advances in Modeling Earth Systems 13 (2), e2020MS002405, 2021
Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations: General algorithms and Lorenz 96 case study (v1. 0)
S Rasp
Geoscientific Model Development 13 (5), 2185-2196, 2020
Achieving conservation of energy in neural network emulators for climate modeling
T Beucler, S Rasp, M Pritchard, P Gentine
arXiv preprint arXiv:1906.06622, 2019
Combining crowdsourcing and deep learning to explore the mesoscale organization of shallow convection
S Rasp, H Schulz, S Bony, B Stevens
Bulletin of the American Meteorological Society 101 (11), E1980-E1995, 2020
Potential and limitations of machine learning for modeling warm‐rain cloud microphysical processes
A Seifert, S Rasp
Journal of Advances in Modeling Earth Systems, e2020MS002301, 2020
Relative contribution of soil moisture, boundary‐layer and microphysical perturbations on convective predictability in different weather regimes
C Keil, F Baur, K Bachmann, S Rasp, L Schneider, C Barthlott
Quarterly Journal of the Royal Meteorological Society 145 (724), 3102-3115, 2019
Increasing the accuracy and resolution of precipitation forecasts using deep generative models
I Price, S Rasp
International conference on artificial intelligence and statistics, 10555-10571, 2022
Stochastic parameterization of processes leading to convective initiation in kilometer-scale models
M Hirt, S Rasp, U Blahak, GC Craig
Monthly Weather Review 147 (11), 3917-3934, 2019
Variability and clustering of midlatitude summertime convection: Testing the Craig and Cohen theory in a convection-permitting ensemble with stochastic boundary layer perturbations
S Rasp, T Selz, GC Craig
Journal of the Atmospheric Sciences 75 (2), 691-706, 2018
Towards physically-consistent, data-driven models of convection
T Beucler, M Pritchard, P Gentine, S Rasp
Igarss 2020-2020 ieee international geoscience and remote sensing symposium …, 2020
Climate-invariant machine learning
T Beucler, P Gentine, J Yuval, A Gupta, L Peng, J Lin, S Yu, S Rasp, ...
Science Advances 10 (6), eadj7250, 2024
WeatherBench 2: A benchmark for the next generation of data‐driven global weather models
S Rasp, S Hoyer, A Merose, I Langmore, P Battaglia, T Russell, ...
Journal of Advances in Modeling Earth Systems 16 (6), e2023MS004019, 2024
Machine learning for clouds and climate
T Beucler, I Ebert‐Uphoff, S Rasp, M Pritchard, P Gentine
Clouds and their climatic impacts: Radiation, circulation, and precipitation …, 2023
Neural general circulation models
D Kochkov, J Yuval, I Langmore, P Norgaard, J Smith, G Mooers, J Lottes, ...
arXiv preprint arXiv:2311.07222, 2023
Convective and slantwise trajectory ascent in convection-permitting simulations of midlatitude cyclones
S Rasp, T Selz, GC Craig
Monthly Weather Review 144 (10), 3961-3976, 2016
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