Machine learning for climate physics and simulations
We discuss the emerging advances and opportunities at the intersection of machine
learning (ML) and climate physics, highlighting the use of ML techniques, including …
learning (ML) and climate physics, highlighting the use of ML techniques, including …
Interpretable machine learning for weather and climate prediction: A review
Advanced machine learning models have recently achieved high predictive accuracy for
weather and climate prediction. However, these complex models often lack inherent …
weather and climate prediction. However, these complex models often lack inherent …
Gencast: Diffusion-based ensemble forecasting for medium-range weather
Probabilistic weather forecasting is critical for decision-making in high-impact domains such
as flood forecasting, energy system planning or transportation routing, where quantifying the …
as flood forecasting, energy system planning or transportation routing, where quantifying the …
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) …
Deep generative data assimilation in multimodal setting
Robust integration of physical knowledge and data is key to improve computational
simulations such as Earth system models. Data assimilation is crucial for achieving this goal …
simulations such as Earth system models. Data assimilation is crucial for achieving this goal …
Fengwu-ghr: Learning the kilometer-scale medium-range global weather forecasting
Kilometer-scale modeling of global atmosphere dynamics enables fine-grained weather
forecasting and decreases the risk of disastrous weather and climate activity. Therefore …
forecasting and decreases the risk of disastrous weather and climate activity. Therefore …
Improving global weather and ocean wave forecast with large artificial intelligence models
The rapid advancement of artificial intelligence technologies, particularly in recent years,
has led to the emergence of several large parameter artificial intelligence weather forecast …
has led to the emergence of several large parameter artificial intelligence weather forecast …
Interpretable structural model error discovery from sparse assimilation increments using spectral bias‐reduced neural networks: A quasi‐geostrophic turbulence test …
R Mojgani, A Chattopadhyay… - Journal of Advances in …, 2024 - Wiley Online Library
Earth system models suffer from various structural and parametric errors in their
representation of nonlinear, multi‐scale processes, leading to uncertainties in their long …
representation of nonlinear, multi‐scale processes, leading to uncertainties in their long …
Exploring the design space of deep-learning-based weather forecasting systems
Despite tremendous progress in developing deep-learning-based weather forecasting
systems, their design space, including the impact of different design choices, is yet to be well …
systems, their design space, including the impact of different design choices, is yet to be well …
Is Artificial Intelligence Providing the Second Revolution for Weather Forecasting?
The rapid advancement of artificial intelligence technologies, particularly in recent years,
has led to the emergence of several large parameter artificial intelligence weather forecast …
has led to the emergence of several large parameter artificial intelligence weather forecast …