Stephan Rasp
Stephan Rasp
Senior Data Scientist @ClimateAi
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
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
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
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
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
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
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
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
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
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
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
Comparison of methods accounting for subgrid-scale model error in convective-scale data assimilation
Y Zeng, T Janjić, A de Lozar, S Rasp, U Blahak, A Seifert, GC Craig
Monthly Weather Review 148 (6), 2457-2477, 2020
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
Training a convolutional neural network to conserve mass in data assimilation
Y Ruckstuhl, T Janjić, S Rasp
Nonlinear Processes in Geophysics Discussions 2020, 1-15, 2020
Machine Learning for Clouds and Climate (Invited Chapter for the AGU Geophysical Monograph Series" Clouds and Climate")
T Beucler, I Ebert-Uphoff, S Rasp, M Pritchard, P Gentine
Using neural networks to improve simulations in the gray zone
R Kriegmair, Y Ruckstuhl, S Rasp, G Craig
Nonlinear Processes in Geophysics Discussions, 1-19, 2021
Deep Learning based cloud parametrization for the Community Atmosphere Model
G Behrens, V Eyring, P Gentine, MS Pritchard, T Beucler, S Rasp
EGU2020, 2020
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