Search Results for author: Stephan Rasp

Found 14 papers, 12 papers with code

Neural General Circulation Models for Weather and Climate

1 code implementation13 Nov 2023 Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith, Griffin Mooers, Milan Klöwer, James Lottes, Stephan Rasp, Peter Düben, Sam Hatfield, Peter Battaglia, Alvaro Sanchez-Gonzalez, Matthew Willson, Michael P. Brenner, Stephan Hoyer

Here we present the first GCM that combines a differentiable solver for atmospheric dynamics with ML components, and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best ML and physics-based methods.

Physical Simulations Weather Forecasting

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

1 code implementation29 Aug 2023 Stephan Rasp, Stephan Hoyer, Alexander Merose, Ian Langmore, Peter Battaglia, Tyler Russel, Alvaro Sanchez-Gonzalez, Vivian Yang, Rob Carver, Shreya Agrawal, Matthew Chantry, Zied Ben Bouallegue, Peter Dueben, Carla Bromberg, Jared Sisk, Luke Barrington, Aaron Bell, Fei Sha

WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to accelerate progress in data-driven weather modeling.

Weather Forecasting

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

1 code implementation2 May 2022 Sagar Garg, Stephan Rasp, Nils Thuerey

WeatherBench is a benchmark dataset for medium-range weather forecasting of geopotential, temperature and precipitation, consisting of preprocessed data, predefined evaluation metrics and a number of baseline models.

Weather Forecasting

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

1 code implementation23 Mar 2022 Ilan Price, Stephan Rasp

Accurately forecasting extreme rainfall is notoriously difficult, but is also ever more crucial for society as climate change increases the frequency of such extremes.

Generative Adversarial Network Super-Resolution

Climate-Invariant Machine Learning

1 code implementation14 Dec 2021 Tom Beucler, Pierre Gentine, Janni Yuval, Ankitesh Gupta, Liran Peng, Jerry Lin, Sungduk Yu, Stephan Rasp, Fiaz Ahmed, Paul A. O'Gorman, J. David Neelin, Nicholas J. Lutsko, Michael Pritchard

Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates.

BIG-bench Machine Learning

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

3 code implementations19 Aug 2020 Stephan Rasp, Nils Thuerey

Numerical weather prediction has traditionally been based on physical models of the atmosphere.

Atmospheric and Oceanic Physics

Towards Physically-consistent, Data-driven Models of Convection

4 code implementations20 Feb 2020 Tom Beucler, Michael Pritchard, Pierre Gentine, Stephan Rasp

Data-driven algorithms, in particular neural networks, can emulate the effect of sub-grid scale processes in coarse-resolution climate models if trained on high-resolution climate simulations.

WeatherBench: A benchmark dataset for data-driven weather forecasting

3 code implementations2 Feb 2020 Stephan Rasp, Peter D. Dueben, Sebastian Scher, Jonathan A. Weyn, Soukayna Mouatadid, Nils Thuerey

Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains.

Weather Forecasting

Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems

4 code implementations3 Sep 2019 Tom Beucler, Michael Pritchard, Stephan Rasp, Jordan Ott, Pierre Baldi, Pierre Gentine

Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically-inconsistent results when violating fundamental constraints.

Computational Physics Atmospheric and Oceanic Physics

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

1 code implementation2 Jul 2019 Stephan Rasp

Here, I propose online learning as a way to combat these issues.

Atmospheric and Oceanic Physics Computational Physics

Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling

no code implementations15 Jun 2019 Tom Beucler, Stephan Rasp, Michael Pritchard, Pierre Gentine

Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models.

Combining crowd-sourcing and deep learning to explore the meso-scale organization of shallow convection

no code implementations5 Jun 2019 Stephan Rasp, Hauke Schulz, Sandrine Bony, Bjorn Stevens

In particular, the focus is on the organization of shallow cumulus convection in the trade wind regions.

Deep learning to represent sub-grid processes in climate models

3 code implementations12 Jun 2018 Stephan Rasp, Michael S. Pritchard, Pierre Gentine

We train a deep neural network to represent all atmospheric sub-grid processes in a climate model by learning from a multi-scale model in which convection is treated explicitly.

Neural networks for post-processing ensemble weather forecasts

1 code implementation23 May 2018 Stephan Rasp, Sebastian Lerch

Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts.

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