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Found 12 papers, 2 papers with code
Fast, Scale-Adaptive, and Uncertainty-Aware Downscaling of Earth System Model Fields with Generative Foundation Models
Accurate and high-resolution Earth system model (ESM) simulations are essential to assess the ecological and socio-economic impacts of anthropogenic climate change, but are computationally too expensive.
ResoNet: Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer Networks
Recent studies have shown that deep learning (DL) models can skillfully predict the El Ni\~no-Southern Oscillation (ENSO) forecasts over 1. 5 years ahead.
Reconstructing Historical Climate Fields With Deep Learning
Historical records of climate fields are often sparse due to missing measurements, especially before the introduction of large-scale satellite missions.
When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System
Critically, the scalability and generalizability of GFMs empower them to address a wide array of prediction, simulation, and decision tasks related to the intricate interactions among Earth system components.
Exploring Geometric Deep Learning For Precipitation Nowcasting
Motivated by this, we explore a geometric deep learning-based temporal Graph Convolutional Network (GCN) for precipitation nowcasting.
Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints
Many successful methods to learn dynamical systems from data have recently been introduced.
Deep learning for bias-correcting CMIP6-class Earth system models
The accurate representation of precipitation in Earth system models (ESMs) is crucial for reliable projections of the ecological and socioeconomic impacts in response to anthropogenic global warming.
Differentiable Programming for Earth System Modeling
Earth System Models (ESMs) are the primary tools for investigating future Earth system states at time scales from decades to centuries, especially in response to anthropogenic greenhouse gas release.
Physically Constrained Generative Adversarial Networks for Improving Precipitation Fields from Earth System Models
Our method outperforms existing ones in correcting local distributions, and leads to strongly improved spatial patterns especially regarding the intermittency of daily precipitation.
Deep Learning for Improving Numerical Weather Prediction of Heavy Rainfall
The accurate prediction of rainfall, and in particular of the heaviest rainfall events, remains challenging for numerical weather prediction (NWP) models.
Will Artificial Intelligence supersede Earth System and Climate Models?
We outline a perspective of an entirely new research branch in Earth and climate sciences, where deep neural networks and Earth system models are dismantled as individual methodological approaches and reassembled as learning, self-validating, and interpretable Earth system model-network hybrids.
Deep Graphs - a general framework to represent and analyze heterogeneous complex systems across scales
These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales.
Data Analysis, Statistics and Probability Social and Information Networks Atmospheric and Oceanic Physics Physics and Society
