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ClimateAI: Climate Intelligence Framework
📑 Paper | 🌐 Project Page | 💾 Released Resources | 📦 Repo
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Dataset
| Dataset | Link |
|---|---|
| ClimateAI-ERA5-Downscale | 🤗 |
Models
| Base Model / Training | ClimateAI | ClimateAI++ | ||
|---|---|---|---|---|
| Stage 1 | Stage 2 | Stage 1 | Stage 2 | |
| Pangu-Weather Base | 🤗 | 🤗 | 🤗 | 🤗 |
| GraphCast 1deg | 🤗 | 🤗 | 🤗 | 🤗 |
| FourCastNet v2 | 🤗 | 🤗 | 🤗 | 🤗 |
Introduction
While having high-resolution climate reanalysis data theoretically enables accurate weather predictions, two challenges arise: 1) The computational cost of running full physics simulations at high resolution is prohibitive; 2) Traditional numerical weather prediction models are constrained by simplified parameterizations and lack the adaptability of ML-based approaches. Thus, we adopt a fully transformer-based approach for downscaling climate data using Pangu-Weather architecture, as it has demonstrated state-of-the-art performance on medium-range forecasting while being computationally efficient.
*Due to data licensing constraints from ECMWF, we only release the ERA5-Downscale subset (this page) of the full dataset.
License
The license for this dataset is CC-BY-NC-SA-4.0.
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