1.1k citations · 3.5k across the 16 of their papers we have counts for
17 papers
WeatherNext 3: Increasing resolution and performance of global weather models with raw observations
Stephan Rasp, Boris Babenko, Dominic Masters +22
State-of-the-art AI weather models have shown impressive medium-range forecast skill and computational efficiency, but suffer two key shortcomings: their forecasts have lower spati…
An Operational Deep Learning System for Satellite-Based High-Resolution Global Nowcasting
Shreya Agrawal, Mohammed Alewi Hassen, Emmanuel Asiedu Brempong +16
Precipitation nowcasting, which predicts rainfall up to a few hours ahead, is a critical tool for vulnerable communities in the Global South frequently exposed to intense, rapidly…
Neural General Circulation Models for Weather and Climate
Dmitrii Kochkov, Janni Yuval, Ian Langmore +13
General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics…
WeatherBench 2: A benchmark for the next generation of data-driven global weather models
Stephan Rasp, Stephan Hoyer, Alexander Merose +15
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 dat…
The role of internal variability in global climate projections of extreme events
Mackenzie L. Blanusa, Carla J. López-Zurita, Stephan Rasp
Climate projection uncertainty can be partitioned into model uncertainty, scenario uncertainty and internal variability. Here, we investigate the different sources of uncertainty i…
WeatherBench Probability: A benchmark dataset for probabilistic medium-range weather forecasting along with deep learning baseline models
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 metr…
Increasing the accuracy and resolution of precipitation forecasts using deep generative models
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. Global numer…
Climate-Invariant Machine Learning
Tom Beucler, Pierre Gentine, Janni Yuval +10
Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models requir…
Data-driven medium-range weather prediction with a Resnet pretrained on climate simulations: A new model for WeatherBench
Stephan Rasp, Nils Thuerey
Numerical weather prediction has traditionally been based on physical models of the atmosphere. Recently, however, the rise of deep learning has created increased interest in purel…
Towards Physically-consistent, Data-driven Models of Convection
Tom Beucler, Michael Pritchard, Pierre Gentine +1
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…
WeatherBench: A benchmark dataset for data-driven weather forecasting
Stephan Rasp, Peter D. Dueben, Sebastian Scher +3
Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains. A natural question to ask is whether data-driven methods could al…
Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems
Tom Beucler, Michael Pritchard, Stephan Rasp +3
Neural networks can emulate nonlinear physical systems with high accuracy, yet they may produce physically-inconsistent results when violating fundamental constraints. Here, we int…
Coupled online learning as a way to tackle instabilities and biases in neural network parameterizations
Stephan Rasp
Over the last couple of years, machine learning parameterizations have emerged as a potential way to improve the representation of sub-grid processes in Earth System Models (ESMs).…
Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling
Tom Beucler, Stephan Rasp, Michael Pritchard +1
Artificial neural-networks have the potential to emulate cloud processes with higher accuracy than the semi-empirical emulators currently used in climate models. However, neural-ne…
Combining crowd-sourcing and deep learning to explore the meso-scale organization of shallow convection
Stephan Rasp, Hauke Schulz, Sandrine Bony +1
Humans excel at detecting interesting patterns in images, for example those taken from satellites. This kind of anecdotal evidence can lead to the discovery of new phenomena. Howev…
Deep learning to represent sub-grid processes in climate models
Stephan Rasp, Michael S. Pritchard, Pierre Gentine
The representation of nonlinear sub-grid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent…
Neural networks for post-processing ensemble weather forecasts
Stephan Rasp, Sebastian Lerch
Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished w…