1 citations · 1 across the 5 of their papers we have counts for
5 papers
Stochastically Perturbed Weights: Ensembles from Deterministic Machine-Learning Weather Models
Simon Adamov, Oliver Fuhrer, Reto Knutti +1
Machine-learning weather models (MLWMs) now match or outperform operational numerical weather prediction (NWP) at global medium-range forecasting, at far lower inference cost. Many…
Machine learning is revolutionizing weather forecasting -- the next step is a change in how we work
Peter Dueben, Peter Bauer, Oliver Fuhrer +2
Following the success of machine learning in producing weather predictions with competitive skill compared to complex traditional systems, this article shifts attention from foreca…
Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting
Firat Ozdemir, Yun Cheng, Salman Mohebi +11
Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets to enable versatile downstream applications through…
Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings
Simon Adamov, Joel Oskarsson, Leif Denby +8
Machine learning is revolutionizing global weather forecasting, with models that efficiently produce highly accurate forecasts. Apart from global forecasting there is also a large…
Towards Specialized Supercomputers for Climate Sciences: Computational Requirements of the Icosahedral Nonhydrostatic Weather and Climate Model
Torsten Hoefler, Alexandru Calotoiu, Anurag Dipankar +3
We discuss the computational challenges and requirements for high-resolution climate simulations using the Icosahedral Nonhydrostatic Weather and Climate Model (ICON). We define a…