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Retrofitting Earth System Models with Cadence-Limited Neural Operator Updates

arXiv — cs.LG•Thursday, December 4, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
  • A new operator-learning framework has been introduced to enhance Earth-system model (ESM) predictions by addressing limitations such as coarse resolution and uncertain initial states. This framework utilizes a U-Net backbone to develop two architectures, Inception U-Net and a multi-scale network, which improve bias-correction tendencies during model integration. The operators were trained on E3SM simulations aligned with ERA5 reanalysis data.
  • This development is significant as it allows for more accurate and reliable predictions in Earth-system modeling, which is crucial for understanding climate dynamics and informing policy decisions. The improved architectures outperform traditional U-Net baselines, indicating a step forward in model performance.
  • The integration of advanced AI techniques in Earth-system modeling reflects a broader trend in leveraging machine learning for environmental applications. Similar advancements in AI are being utilized for near-real-time flood mapping and enhancing weather forecasts, highlighting the growing intersection of technology and climate science in addressing global challenges.
— via World Pulse Now AI Editorial System
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