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.
This study analyses kinetic energy (KE) spectra, difference kinetic energy (DKE) spectra, and signatures of KE transfer across spatial scales in four state-of-the-art probabilistic machine learning we...
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: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).
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: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:2608.30795v1 Announce Type: cross Abstract: End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the nume...
arXiv:2511. 23043v2 Announce Type: replace-cross Abstract: We present a probabilistic data-driven weather model providing ensembles of high spatial resolution realizations of 87 variables at arbitrary ensemble size and forecast length.
arXiv:2606. 10642v1 Announce Type: new Abstract: Machine learning weather prediction (MLWP) models have achieved impressive forecasting performance at a small fraction of the computational costs required for traditional physics-based methods.
arXiv:2510. 02415v3 Announce Type: replace-cross Abstract: Machine learning models for the global atmosphere that are capable of producing stable, multi-year simulations of Earth's climate have recently been developed.
arXiv:2609.25505v1 Announce Type: cross Abstract: Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics nu...
The paper introduces an explainable contrastive learning model that maps five monthly cloud and radiation fields into a shared representation space to analyze perturbed parameter ensembles (PPEs) in climate simulations. Trained on two 100-member Community Atmosphere Model version 6 PPEs differing only in the warm rain microphysics scheme (KK2000 vs. TAU-ML), the model achieves over 94% linear classification accuracy while preserving seasonal variability and ensemble spread. The learned representations place satellite observations on the same low‑dimensional manifold as the PPEs, with TAU-ML PPE closer to observations, and Integrated Gradients attribution identifies key regional contributions linked to cloud microphysics, boundary layer turbulence, and deep convection.