Towards neural Earth system modelling by integrating artificial intelligence in Earth system science
Earth system models (ESMs) are our main tools for quantifying the physical state of the Earth
and predicting how it might change in the future under ongoing anthropogenic forcing. In …
and predicting how it might change in the future under ongoing anthropogenic forcing. In …
The contribution of data-driven technologies in achieving the sustainable development goals
The United Nations' Sustainable Development Goals (SDGs) set out to improve the quality of
life of people in developed, emerging, and develo** countries by covering social and …
life of people in developed, emerging, and develo** countries by covering social and …
Learning skillful medium-range global weather forecasting
Global medium-range weather forecasting is critical to decision-making across many social
and economic domains. Traditional numerical weather prediction uses increased compute …
and economic domains. Traditional numerical weather prediction uses increased compute …
Neural general circulation models for weather and climate
General circulation models (GCMs) are the foundation of weather and climate prediction,.
GCMs are physics-based simulators that combine a numerical solver for large-scale …
GCMs are physics-based simulators that combine a numerical solver for large-scale …
Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators
FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather
forecasting model that provides accurate short to medium-range global predictions at …
forecasting model that provides accurate short to medium-range global predictions at …
ClimaX: A foundation model for weather and climate
Most state-of-the-art approaches for weather and climate modeling are based on physics-
informed numerical models of the atmosphere. These approaches aim to model the non …
informed numerical models of the atmosphere. These approaches aim to model the non …
Spherical fourier neural operators: Learning stable dynamics on the sphere
Abstract Fourier Neural Operators (FNOs) have proven to be an efficient and effective
method for resolution-independent operator learning in a broad variety of application areas …
method for resolution-independent operator learning in a broad variety of application areas …
Pde-refiner: Achieving accurate long rollouts with neural pde solvers
Time-dependent partial differential equations (PDEs) are ubiquitous in science and
engineering. Recently, mostly due to the high computational cost of traditional solution …
engineering. Recently, mostly due to the high computational cost of traditional solution …
Forecasting global weather with graph neural networks
R Keisler - arxiv preprint arxiv:2202.07575, 2022 - arxiv.org
We present a data-driven approach for forecasting global weather using graph neural
networks. The system learns to step forward the current 3D atmospheric state by six hours …
networks. The system learns to step forward the current 3D atmospheric state by six hours …
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 days) weather forecasting
benchmark proposed by (Rasp et al., 2020, https://doi. org/10.1029/2020ms002203) …
benchmark proposed by (Rasp et al., 2020, https://doi. org/10.1029/2020ms002203) …