The results suggest that bidirectional Vision 15 Mamba is a strong backbone for neural weather prediction — achieving better accuracy and growing computational efficiency, consistent with a more suitable spatial inductive bias for atmospheric modeling.
The National Oceanic and Atmospheric Administration (NOAA) employs independent prediction systems for distinct forecast products. While some separation is practical, we argue that combining short- and medium-range weather into a single prediction system would provide the public with a useful distillation of global weat...
Timothy A. Smith, Mariah Pope, Sergey Frolov et al.· 2 citations
This work presents a single-pass model that jointly forecasts GRIDSAT-B1 infrared imagery and four ERA5 atmospheric fields out to nine hours and reward-fine-tuned against a differentiable track error derived from the predicted winds through a steering-flow calculation.
An extensive evaluation, including routine verification against observation, a tropical cyclone case and spectral analysis reveal the strengths and limitations in the representation of atmospheric variability across scales.
Tobias Goecke, M. Jacob, F. Prill et al.· 1 citation
Data-driven machine-learning weather models now rival, and in some respects surpass, operational numerical weather prediction (NWP), yet no single model dominates across variables, pressure levels, lead times, or regions, and the marginal returns from developing ever-larger individual models are diminishing. We present...
Spatio-Temporal U-DeepONet (GenONet), a novel architecture for long-range precipitation forecasting up to 3 hours, specifically designed to produce sharp and physically consistent results, is introduced.
Mohammad Kian Golkar, Luciano Alves de Oliveira, Mohammad Khanjani· 0 citations
Training high-resolution AI-based Earth forecasting models is memory-intensive. Window-based Swin Transformers reduce the quadratic cost of global attention, but existing distributed systems such as AERIS primarily target pixel-level models and do not jointly support convolutional sampling modules and shifted-window ex...
Ruohan Wu, Ziqi Zhu, Yang Zhao et al.· 0 citations
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