Physics-informed machine learning: case studies for weather and climate modelling
Machine learning (ML) provides novel and powerful ways of accurately and efficiently
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …
recognizing complex patterns, emulating nonlinear dynamics, and predicting the spatio …
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 …
Machine learning in weather prediction and climate analyses—applications and perspectives
In this paper, we performed an analysis of the 500 most relevant scientific articles published
since 2018, concerning machine learning methods in the field of climate and numerical …
since 2018, concerning machine learning methods in the field of climate and numerical …
AI-empowered next-generation multiscale climate modelling for mitigation and adaptation
Earth system models have been continously improved over the past decades, but systematic
errors compared with observations and uncertainties in climate projections remain. This is …
errors compared with observations and uncertainties in climate projections remain. This is …
Bridging observations, theory and numerical simulation of the ocean using machine learning
Progress within physical oceanography has been concurrent with the increasing
sophistication of tools available for its study. The incorporation of machine learning (ML) …
sophistication of tools available for its study. The incorporation of machine learning (ML) …
Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision
A promising approach to improve climate‐model simulations is to replace traditional subgrid
parameterizations based on simplified physical models by machine learning algorithms that …
parameterizations based on simplified physical models by machine learning algorithms that …
Stochastic‐deep learning parameterization of ocean momentum forcing
AP Guillaumin, L Zanna - Journal of Advances in Modeling …, 2021 - Wiley Online Library
Coupled climate simulations that span several hundred years cannot be run at a high‐
enough spatial resolution to resolve mesoscale ocean dynamics. Recently, several studies …
enough spatial resolution to resolve mesoscale ocean dynamics. Recently, several studies …
A fortran‐keras deep learning bridge for scientific computing
Implementing artificial neural networks is commonly achieved via high‐level programming
languages such as Python and easy‐to‐use deep learning libraries such as Keras. These …
languages such as Python and easy‐to‐use deep learning libraries such as Keras. These …
Correcting weather and climate models by machine learning nudged historical simulations
Due to limited resolution and inaccurate physical parameterizations, weather and climate
models consistently develop biases compared to the observed atmosphere. Using the …
models consistently develop biases compared to the observed atmosphere. Using the …
[HTML][HTML] Machine learning for numerical weather and climate modelling: a review
CO de Burgh-Day… - Geoscientific Model …, 2023 - gmd.copernicus.org
Abstract Machine learning (ML) is increasing in popularity in the field of weather and climate
modelling. Applications range from improved solvers and preconditioners, to …
modelling. Applications range from improved solvers and preconditioners, to …