7 papers
OpFML: Pipeline for ML-based Operational Inference
Shahbaz Alvi, Giusy Fedele, Gabriele Accarino +3
Machine learning models for climate and Earth science are becoming increasingly capable, yet model deployment into operational use remains a largely unaddressed challenge: general-…
Wavelet Flow Matching for Multi-Scale Physics Emulation
Gabriele Accarino, Juan Nathaniel, Carla Roesch +4
Accurate emulation of multi-scale physical systems governed by PDEs demands models that remain stable over long autoregressive rollouts while preserving fine-scale structures. Dete…
ByteStorm: a multi-step data-driven approach for Tropical Cyclones detection and tracking
Davide Donno, Donatello Elia, Gabriele Accarino +3
Accurate tropical cyclones (TCs) tracking represents a critical challenge in the context of weather and climate science. Traditional tracking schemes mainly rely on subjective thre…
WaveSim: A Wavelet-based Multi-scale Similarity Metric for Weather and Climate Fields
Gabriele Accarino, Viviana Acquaviva, Sara Shamekh +2
We introduce WaveSim, a multi-scale similarity metric for the evaluation of spatial fields in weather and climate applications. WaveSim exploits wavelet transforms to decompose inp…
Transferring climate change physical knowledge
Francesco Immorlano, Veronika Eyring, Thomas le Monnier de Gouville +5
Precise and reliable climate projections are required for climate adaptation and mitigation, but Earth system models still exhibit great uncertainties. Several approaches have been…
MedFormer: a data-driven model for forecasting the Mediterranean Sea
Italo Epicoco, Davide Donno, Gabriele Accarino +14
Accurate ocean forecasting is essential for supporting a wide range of marine applications. Recent advances in artificial intelligence have highlighted the potential of data-driven…