The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
The paper examines kinetic energy (KE) spectra and KE transfer across spatial scales in four leading probabilistic machine learning weather prediction models—NeuralGCM-ENS, FourCastNet 3, AIFS-ENS, and GenCast—by comparing them to the physics‑based IFS‑ENS model. NeuralGCM‑ENS captures the expected upscale KE transfer but underestimates mesoscale KE due to noise injection at its encoder, while AIFS‑ENS, GenCast, and FourCastNet 3 produce realistic KE magnitudes yet fail to show the expected upscale transfer, with AIFS‑ENS and GenCast accumulating KE at high wavenumbers because of spatially uncorrelated stochastic perturbations. All models display an upscale shift of the difference KE (DKE) spectral peak over time, indicating error growth, but none reproduce the rapid initial growth of ensemble spread at small scales associated with the butterfly effect, highlighting a misrepresentation of kinetic energy scale transfer despite overall skillful forecasts.
arXiv:2602. 13416v2 Announce Type: replace Abstract: The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-range forecasting.
arXiv:2601.21151v3 Announce Type: replace Abstract: Machine-learning approaches to weather forecasting often employ a monolithic architecture in which distinct physical mechanisms, such as advection,...
arXiv:2607. 21080v1 Announce Type: new Abstract: Long-horizon weather forecasting is a fundamental challenge in atmospheric science, for which autoregressive Deep Learning Weather Prediction (DLWP) has emerged as the primary paradigm.
arXiv:2606. 08563v1 Announce Type: new Abstract: While global data-driven models excel at predicting continuous atmospheric variables, three-dimensional hydrometeor forecasting remains challenging due to the zero-inflated, long-tailed distributions of these variables.
arXiv:2608. 09971v1 Announce Type: cross Abstract: Over the past few years, the rapid development of machine learning (ML) models for weather forecasting has produced deterministic models whose medium-range skill matches or exceeds that of the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES).