🎙️ Abstract:Embedding physical constraints as inductive priors is key in AI weather forecasting models. Locality—a common prior—relies on local neural interactions like 3D convolutions or attention. ARCHESWEATHER challenges this norm by introducing global vertical interactions, improving efficiency without sacrificing accuracy.📌 Bullet points summary:ARCHESWEATHER is a lightweight, efficient AI model trained at 1.5º resolution with minimal compute (a few GPU-days), offering low-cost inference and strong performance.The Cross-Level Attention (CLA) mechanism enables vertical atmospheric feature interactions, replacing 3D local attention with 2D horizontal attention and column-wise CLA in a 3D Swin U-Net with Earth-specific biases.Ensemble versions (MX4 and LX2) outperform or match IFS HRES and NeuralGCM in RMSE for 1–3 day forecasts on upper-air variables; it gains edge on wind variables at longer lead times.Fine-tuning on post-2007 ERA5 data yields modest gains, pointing to distributional shifts in the dataset.A convolutional head with bilinear upsampling avoids checkerboard artifacts, offering cleaner projections. The code is open-source.💡 Big Idea:ARCHESWEATHER shows that global vertical interactions via cross-level attention can outperform traditional locality-based models, paving a path toward more efficient, physically grounded weather forecasting systems.📚 Citation:Mukkavilli, S. Karthik, et al. "Ai foundation models for weather and climate: Applications, design, and implementation." arXiv preprint arXiv:2309.10808 (2023). DOI: 10.48550/arXiv.2405.14527

Ver todos los episodios