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--- |
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size_categories: |
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- 100K<n<1M |
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tags: |
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- Helio |
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- Pretraining |
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- faoundation-model |
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- solar |
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- SDO |
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- EUV |
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- magnetogram |
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- multimodal |
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pretty_name: SuryaBench |
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license: cc-by-4.0 |
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--- |
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# ML-Ready Multi-Modal Image Dataset from SDO |
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## Overview |
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This dataset provides machine learning (ML)-ready solar data curated from NASA’s Solar Dynamics Observatory (SDO), covering observations from **May 13, 2010, to Dec 31, 2024**. It includes Level-1.5 processed data from: **Atmospheric Imaging Assembly (AIA)** |
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and **Helioseismic and Magnetic Imager (HMI)**. |
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The dataset is designed to facilitate large-scale learning applications in heliophysics, such as space weather forecasting, unsupervised representation learning, and scientific foundation model development. |
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## Dataset Download Instructions |
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To download the data please visit https://registry.opendata.aws/surya-bench/ |
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- Resource type: `S3 Bucket` |
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- Amazon Resource Name (ARN): `arn:aws:s3:::nasa-surya-bench` |
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- AWS CLI Access (No AWS account required): `aws s3 ls --no-sign-request s3://nasa-surya-bench/` |
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--- |
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## Dataset Structure |
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**Data Variables:** |
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```text |
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- aia94 (y, x) float32 : AIA 94 Å |
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- aia131 (y, x) float32 : AIA 131 Å |
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- aia171 (y, x) float32 : AIA 171 Å |
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- aia193 (y, x) float32 : AIA 193 Å |
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- aia211 (y, x) float32 : AIA 211 Å |
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- aia304 (y, x) float32 : AIA 304 Å |
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- aia335 (y, x) float32 : AIA 335 Å |
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- aia1600 (y, x) float32 : AIA 1600 Å (UV continuum) |
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- hmi_m (y, x) float32 : HMI LOS Magnetogram |
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- hmi_bx (y, x) float32 : HMI Magnetic Field - x component |
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- hmi_by (y, x) float32 : HMI Magnetic Field - y component |
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- hmi_bz (y, x) float32 : HMI Magnetic Field - z component |
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- hmi_v (y, x) float32 : HMI Doppler Velocity |
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``` |
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## Dataset Details |
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| Field | Description | |
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|------------------------|---------------------------------------------| |
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| **Temporal Coverage** | May 13, 2010 – Dec 31, 2024 | |
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| **Data Format** | netCDF (`.nc`), float32 | |
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| **Temporal Granularity**| 12 minutes | |
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| **Data Shape** | `[13, 4096, 4096]` per file | |
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| **Channels** | 13 total (AIA EUV ×8 + HMI magnetograms ×5) | |
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| **Size per File** | ~570 MB | |
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| **Total Size** | ~360TB | |
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--- |
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## 📦 Downstream Data Repositories |
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All the downstream tasks that uses core-sdo dataset can be found in the [SuryaBench Hugging Face Collections](https://huggingface.co/collections/nasa-ibm-ai4science/suryabench) |
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Each sub-dataset targets a specific task within the heliophysics domain: |
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| Repository | Task Description | |
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|------------|------------------| |
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| [`Surya-bench-solarwind`](https://huggingface.co/datasets/nasa-ibm-ai4science/Surya-bench-solarwind) | Solar wind speed prediction with a 4-day forecast horizon. | |
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| [`surya-bench-flare-forecasting`](https://huggingface.co/datasets/nasa-ibm-ai4science/surya-bench-flare-forecasting) | Binary classification for solar flare occurrence within 24 hours. | |
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| [`surya-bench-ar-segmentation`](https://huggingface.co/datasets/nasa-ibm-ai4science/surya-bench-ar-segmentation) | Pixel-wise segmentation of active regions from solar disk images. | |
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| [`euv_spectra`](https://huggingface.co/datasets/nasa-ibm-ai4science/euv-spectra) | Time-aligned Extreme Ultraviolet (EUV) irradiance spectra from NASA’s SDO/EVE instrument. | |
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| [`surya-bench-coronal-extrapolation`](https://huggingface.co/datasets/nasa-ibm-ai4science/surya-bench-coronal-extrapolation) | Magnetic field extrapolation from photosphere to corona. | |
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| [`ar_emergence`](https://huggingface.co/datasets/nasa-ibm-ai4science/ar_emergence) | Forecasting active region emergence based on historical features. | |
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## License |
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This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License. |
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#### Authors |
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Sujit Roy, Dinesha V Hegde, Johannes Schmude, Rohit Lal, Vishal Gaur |
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corr: [email protected] |
