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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 …
[HTML][HTML] Eyes in the sky: Drones applications in the built environment under climate change challenges
This paper reviews the diverse applications of drone technologies in the built environment
and their role in climate change research. Drones, or unmanned aerial vehicles (UAVs) …
and their role in climate change research. Drones, or unmanned aerial vehicles (UAVs) …
[HTML][HTML] Machine learning for numerical weather and climate modelling: a review
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 …
Potential effects of climate change and solar radiation modification on renewable energy resources
Solar radiation modification (SRM) is a possible deliberate approach to decrease or reflect
incoming solar radiation with the goal of reducing global temperatures, which have …
incoming solar radiation with the goal of reducing global temperatures, which have …
Enhancing regional climate downscaling through advances in machine learning
Despite the sophistication of global climate models (GCMs), their coarse spatial resolution
limits their ability to resolve important aspects of climate variability and change at the local …
limits their ability to resolve important aspects of climate variability and change at the local …
Foundation models for weather and climate data understanding: A comprehensive survey
As artificial intelligence (AI) continues to rapidly evolve, the realm of Earth and atmospheric
sciences is increasingly adopting data-driven models, powered by progressive …
sciences is increasingly adopting data-driven models, powered by progressive …
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. Current climate models require …
physical models across past, present, and future climates. Current climate models require …
Ai foundation models for weather and climate: Applications, design, and implementation
Machine learning and deep learning methods have been widely explored in understanding
the chaotic behavior of the atmosphere and furthering weather forecasting. There has been …
the chaotic behavior of the atmosphere and furthering weather forecasting. There has been …
[HTML][HTML] Do data-driven models beat numerical models in forecasting weather extremes? A comparison of IFS HRES, Pangu-Weather, and GraphCast
The last few years have witnessed the emergence of data-driven weather forecast models
capable of competing with–and, in some respects, outperforming–physics-based numerical …
capable of competing with–and, in some respects, outperforming–physics-based numerical …
Hydroclimate volatility on a warming Earth
Hydroclimate volatility refers to sudden, large and/or frequent transitions between very dry
and very wet conditions. In this Review, we examine how hydroclimate volatility is …
and very wet conditions. In this Review, we examine how hydroclimate volatility is …