Pushing the frontiers in climate modelling and analysis with machine learning
Climate modelling and analysis are facing new demands to enhance projections and
climate information. Here we argue that now is the time to push the frontiers of machine …
climate information. Here we argue that now is the time to push the frontiers of machine …
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 …
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
Modern climate projections lack adequate spatial and temporal resolution due to
computational constraints. A consequence is inaccurate and imprecise predictions of critical …
computational constraints. A consequence is inaccurate and imprecise predictions of critical …
[PDF][PDF] Systematic sampling and validation of machine Learning-Parameterizations in climate models
Progress in hybrid physics-machine learning (ML) climate simulations has been limited by
the difficulty of obtaining performant coupled (ie online) simulations. While evaluating …
the difficulty of obtaining performant coupled (ie online) simulations. While evaluating …
A multi-head attention neural network with non-linear correlation approach for time series causal discovery
N Irribarra, K Michell, C Bermeo, W Kristjanpoller - Applied Soft Computing, 2024 - Elsevier
This paper presents a causal discovery model for time series analysis, which can find
several causal lags of one variable over another, presenting the results by means of a …
several causal lags of one variable over another, presenting the results by means of a …
Data imbalance, uncertainty quantification, and generalization via transfer learning in data-driven parameterizations: Lessons from the emulation of gravity wave …
Neural networks (NNs) are increasingly used for data-driven subgrid-scale parameterization
in weather and climate models. While NNs are powerful tools for learning complex nonlinear …
in weather and climate models. While NNs are powerful tools for learning complex nonlinear …
Data imbalance, uncertainty quantification, and transfer learning in data‐driven parameterizations: Lessons from the emulation of gravity wave momentum transport in …
Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale
parameterizations in weather and climate models. While NNs are powerful tools for learning …
parameterizations in weather and climate models. While NNs are powerful tools for learning …
Houston, we have a problem: can satellite information bridge the climate-related data gap?
A Alonso-Robisco, JM Carbo… - … /Banco de España …, 2024 - repositorio.bde.es
Central banks and international supervisors have identified the difficulty of obtaining climate
information as one of the key obstacles to the development of green financial products and …
information as one of the key obstacles to the development of green financial products and …
Causal hybrid modeling with double machine learning—applications in carbon flux modeling
Hybrid modeling integrates machine learning with scientific knowledge to enhance
interpretability, generalization, and adherence to natural laws. Nevertheless, equifinality and …
interpretability, generalization, and adherence to natural laws. Nevertheless, equifinality and …
Interpretable multiscale machine learning‐based parameterizations of convection for ICON
Abstract Machine learning (ML)‐based parameterizations have been developed for Earth
System Models (ESMs) with the goal to better represent subgrid‐scale processes or to …
System Models (ESMs) with the goal to better represent subgrid‐scale processes or to …