TL;DR
CREDIT is a scalable, user-friendly AI framework for atmospheric modeling that enhances weather prediction accuracy and efficiency by supporting advanced models like WXFormer and FUXI, outperforming traditional systems.
Contribution
The paper introduces CREDIT, a flexible platform for training and deploying AI-based atmospheric models, and demonstrates its effectiveness with novel models like WXFormer and FUXI.
Findings
WXFormer and FUXI outperform IFS HRES in 10-day forecasts.
CREDIT enables scalable, modular AI model development for weather prediction.
Models trained on ERA5 data show improved accuracy and efficiency.
Abstract
Recent advancements in artificial intelligence (AI) for numerical weather prediction (NWP) have significantly transformed atmospheric modeling. AI NWP models outperform traditional physics-based systems, such as the Integrated Forecast System (IFS), across several global metrics while requiring fewer computational resources. However, existing AI NWP models face limitations related to training datasets and timestep choices, often resulting in artifacts that reduce model performance. To address these challenges, we introduce the Community Research Earth Digital Intelligence Twin (CREDIT) framework, developed at NSF NCAR. CREDIT provides a flexible, scalable, and user-friendly platform for training and deploying AI-based atmospheric models on high-performance computing systems. It offers an end-to-end pipeline for data preprocessing, model training, and evaluation, democratizing access to…
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Code & Models
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Taxonomy
MethodsAttention Is All You Need · Softmax · Linear Layer · Dense Connections · Layer Normalization · Multi-Head Attention · Residual Connection · Vision Transformer
