S. Rasp

发表

Combining crowd-sourcing and deep learning to understand meso-scale organization of shallow convection pdf

Stephan Rasp, Bjorn Stevens, Sandrine Bony, 2019, Bulletin of the American Meteorological Society.

Machine Learning for Clouds and Climate

I. Ebert‐Uphoff, P. Gentine, S. Rasp, 2020 .

Convective and Slantwise Trajectory Ascent in Convection-Permitting Simulations of Midlatitude Cyclones

S. Rasp, G. Craig, T. Selz, 2016 .

Stochastic Parameterization of Processes Leading to Convective Initiation in Kilometer-Scale Models

U. Blahak, S. Rasp, G. Craig, 2019, Monthly Weather Review.

Comparison of Methods Accounting for Subgrid-Scale Model Error in Convective-Scale Data Assimilation

U. Blahak, S. Rasp, T. Janjić, 2020 .

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, G. Craig, T. Selz, 2017 .

Could machine learning break the convection deadlock?

P. Gentine, S. Rasp, M. Pritchard, 2018 .

Relative contribution of soil moisture, boundary‐layer and microphysical perturbations on convective predictability in different weather regimes

S. Rasp, C. Keil, C. Barthlott, 2019, Quarterly Journal of the Royal Meteorological Society.

Emerging Data Science and Machine Learning Opportunities in the Weather and Climate Sciences

Karthik Kashinath, Anuj Karpatne, Kevin A. Reed, 2018 .

Online learning as a way to tackle instabilities and biases in neural network parameterizations

S. Rasp, 2019 .

The role of internal variability in global climate projections of extreme events

S. Rasp, Mackenzie L. Blanusa, Carla J. L'opez-Zurita, 2022, 2208.08275.

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, 2019, Geoscientific Model Development.

Towards Physically-Consistent, Data-Driven Models of Convection

P. Gentine, S. Rasp, M. Pritchard, 2020, IEEE International Geoscience and Remote Sensing Symposium.

Outcomes of the WMO Prize Challenge to Improve Sub-Seasonal to Seasonal Predictions Using Artificial Intelligence

S. Rasp, B. Denis, R. Roškar, 2022, Bulletin of the American Meteorological Society.

Variability and clustering of mid-latitude summertime convection - Metadata

S. Rasp, 2017 .

Could Machine Learning Break the Convection Parameterization Deadlock?

Pierre Gentine, Michael S. Pritchard, S. Rasp, 2018, Geophysical Research Letters.

Data‐Driven Medium‐Range Weather Prediction With a Resnet Pretrained on Climate Simulations: A New Model for WeatherBench

Nils Thuerey, Stephan Rasp, S. Rasp, 2020, Journal of Advances in Modeling Earth Systems.

WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting

Soukayna Mouatadid, Nils Thuerey, Sebastian Scher, 2020, Journal of Advances in Modeling Earth Systems.

Internal variability plays a dominant role in global climate projections of temperature and precipitation extremes

S. Rasp, Mackenzie L. Blanusa, Carla J. López-Zurita, 2023, Climate Dynamics.

Neural networks for post-processing ensemble weather forecasts pdf

Stephan Rasp, Sebastian Lerch, S. Lerch, 2018, Monthly Weather Review.

Deep learning to represent subgrid processes in climate models

Pierre Gentine, Michael S. Pritchard, Stephan Rasp, 2018, Proceedings of the National Academy of Sciences.

Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems.

Pierre Baldi, Pierre Gentine, Tom Beucler, 2019, Physical review letters.

Increasing the accuracy and resolution of precipitation forecasts using deep generative models

S. Rasp, Ilan Price, 2022, AISTATS.

Climate-Invariant Machine Learning pdf

Janni Yuval, Paul A. O'Gorman, Pierre Gentine, 2021, arXiv.org.

WeatherBench Probability: A benchmark dataset for probabilistic medium-range weather forecasting along with deep learning baseline models

S. Rasp, Nils Thuerey, Sagar Garg, 2022, ArXiv.

Machine Learning for Clouds and Climate (Invited Chapter for the AGU Geophysical Monograph Series "Clouds and Climate")

Imme Ebert-Uphoff, Pierre Gentine, Tom Beucler, 2021 .

Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling pdf

Pierre Gentine, Tom Beucler, Stephan Rasp, 2019, ArXiv.

JiaweiZhuang/xESMF: v0.3.0 Adding ESMF.LocStream capabilities

S. Rasp, Jiawei Zhuang, A. Jüling, 2020 .

Potential and Limitations of Machine Learning for Modeling Warm‐Rain Cloud Microphysical Processes

Axel Seifert, Stephan Rasp, S. Rasp, 2020, Journal of Advances in Modeling Earth Systems.

Combining Crowdsourcing and Deep Learning to Explore the Mesoscale Organization of Shallow Convection

Stephan Rasp, Sandrine Bony, Bjorn Stevens, 2019, Bulletin of the American Meteorological Society.

Climate-invariant machine learning

P. Gentine, S. Rasp, M. Pritchard, 2021, Science advances.

Using neural networks to improve simulations in the gray zone

Stephan Rasp, Yvonne Ruckstuhl, George Craig, 2021, Nonlinear Processes in Geophysics.

Training a convolutional neural network to conserve mass in data assimilation

Stephan Rasp, Yvonne Ruckstuhl, Tijana Janjic, 2020, Nonlinear Processes in Geophysics.

WeatherBench 2: A benchmark for the next generation of data-driven global weather models

P. Battaglia, Alvaro Sanchez-Gonzalez, C. Bromberg, 2023, ArXiv.

Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution

Nils Thuerey, Stephan Rasp, S. Rasp, 2020, 2008.08626.