Hugging Face Trending Papers
GraphCast-Mamba: Learning Non-Markovian Corrections for Weather Forecasting
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arXiv Machine Learning
4d ago
GraphCast-Mamba: Learning Non-Markovian Corrections for Weather Forecasting
arXiv:2610.28512v1 Announce Type: new Abstract: Unresolved atmospheric processes can introduce history dependence beyond the short input windows used by many weather forecasting models. We introduce...
arXiv Machine Learning
Jun 2
Atmospheric Predictability Beyond 30 Days with Machine Learning
arXiv:2504. 20238v2 Announce Type: replace-cross Abstract: Atmospheric predictability research has long held that rapid error growth at small spatial scales imposes an intrinsic limit of roughly two weeks on deterministic weather forecast skill.
arXiv AI
Aug 6
MarsCast: Transfer Learning of AI Weather Foundation Models to Planetary Atmospheres
arXiv:2608. 05054v1 Announce Type: cross Abstract: We investigate the transferability of Earth weather foundation models to planetary atmospheres by adapting the GraphCast graph neural weather forecasting model to Mars.
arXiv Machine Learning
Jun 5
Performance Evaluation of GraphCast for Medium-Range Weather Forecasting over Brazil
arXiv:2606. 06348v1 Announce Type: new Abstract: The paradigm of global weather forecasting is rapidly shifting with the emergence of Machine Learning Weather Prediction models (MLWP).
Hugging Face Trending Papers
Jun 4
Performance Evaluation of GraphCast for Medium-Range Weather Forecasting over Brazil
The paradigm of global weather forecasting is rapidly shifting with the emergence of Machine Learning Weather Prediction models (MLWP). While these data-driven architectures demonstrate remarkable global skill, regional benchmarks in the Global South remain scarce, leaving their efficacy in complex, highly convective environments largely unverified.
arXiv Machine Learning
Sep 25
Improving global precipitation forecasts with an AI weather model trained on satellite observations
The paper presents Laxmi, a retrained version of the AIFS weather model that uses satellite-based precipitation observations instead of ERA5 reanalysis data. Laxmi achieves a 19% improvement in global probabilistic accuracy, reduces drizzle overprediction by 33%, and boosts the 95th percentile Brier skill score by 57%. In a case study of 10 Indian tropical storms, Laxmi accurately forecasted 150 mm event-total precipitation in 7 events, outperforming both the original AIFS and the leading physical model IFS.