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Found 14 papers, 12 papers with code
Neural General Circulation Models for Weather and Climate
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.
WeatherBench 2: A benchmark for the next generation of data-driven global weather models
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.
WeatherBench Probability: A benchmark dataset for probabilistic medium-range weather forecasting along with deep learning baseline models
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.
Increasing the accuracy and resolution of precipitation forecasts using deep generative models
Accurately forecasting extreme rainfall is notoriously difficult, but is also ever more crucial for society as climate change increases the frequency of such extremes.
Climate-Invariant Machine Learning
Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates.
Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution
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
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
Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains.
Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems
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
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
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
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
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
Ensemble weather predictions require statistical post-processing of systematic errors to obtain reliable and accurate probabilistic forecasts.
