arXiv:2606. 26421v1 Announce Type: new Abstract: State-of-the-art medium-range AI weather models can outperform traditional Numerical Weather Prediction (NWP) but require massive training budgets.
By Cristiana Diaconu, Jonas Scholz, Aliaksandra Shysheya, Stratis Markou, Payel Mukhopadhyay, Miles Cranmer, Richard E. Turner
State-of-the-art medium-range AI weather models can outperform traditional Numerical Weather Prediction (NWP) but require massive training budgets. This restricts usage for under-resourced groups and severely limits fast model iteration.
arXiv:2510. 09484v3 Announce Type: replace Abstract: Limited-Area Models (LAMs) enable weather forecasting over regional domains at higher resolutions than what is computationally feasible for global models.
By Erik Larsson, Joel Oskarsson, Tomas Landelius, Fredrik Lindsten
arXiv:2608. 09948v1 Announce Type: cross Abstract: No single AI weather model excels at all variables, pressure levels, and lead times.
By Qiang Wu, Han Li, Jianping Huang
arXiv:2608. 06241v1 Announce Type: new Abstract: Existing machine-learning weather forecasting models rely on predetermined and fixed autoregressive timesteps.
By Sam Levang, Fran Bartolic, Ty Dickinson, Chase Dwelle, Paulius Rauba, Viktor Cikojevic
arXiv:2606. 02663v1 Announce Type: cross Abstract: Recent advances in machine learning have produced probabilistic weather forecasting models comparable to state-of-the-art numerical weather predictors.
By Saptarishi Dhanuka (Ashoka University), Sarvesh Iyer (Ashoka University), Manmeet Singh (Western Kentucky University), Mihir More (Ashoka University), Rushil Gupta (Ashoka University), Dhruman Gupta (Ashoka University), Parthasarathi Mukhopadhyay (Ashoka University), Sandeep Juneja (Ashoka University)