OneScience-Group/Surya

Surya

Model Introduction

Surya is a spatiotemporal foundation model for heliophysics and space weather research. It learns solar dynamics from multi-instrument SDO observations. The model combines spectral gating with long-short attention to predict future solar images from two historical time steps and supports autoregressive multi-step forecasting.

Paper: Surya: Foundation Model for Heliophysics
https://ntrs.nasa.gov/citations/20250008498

Model Description

Surya was proposed by institutions including NASA, IBM Research, and the University of Alabama in Huntsville. The model uses eight AIA channels and five HMI products from SDO as training data. It is suitable for one-step prediction and autoregressive multi-step forecasting of solar images.

Use Cases

Scenario	Description
Solar dynamics forecasting	Predict future multichannel solar images from two historical time steps.
Space weather research	Analyze solar activity and errors at different forecast lead times.
SDO multi-instrument modeling	Jointly process multichannel observations from AIA and HMI.
Solar flare forecasting	Fine-tune model representations to predict M-class and X-class solar flares within future time periods.
Solar active region segmentation	Fine-tune on magnetograms to segment solar active regions and polarity inversion lines.
Local quick validation	Use synthetic data to validate training, inference, evaluation, and visualization.
Multi-GPU training	Launch distributed data-parallel training with torchrun.

Usage Guide

1. OneCode Usage

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2. Manual Installation and Usage

Hardware Requirements

• A GPU or DCU is recommended.
• CPU can be used for pipeline validation with the current default small configuration.
• Training the original paper configuration with 4096x4096 resolution and 366 million parameters requires large-scale multi-GPU computing resources.

Download the Model Package

hf download OneScience-Group/Surya --local-dir ./Surya
cd Surya

Install the Runtime Environment

DCU Environment

conda create -n onescience311 python=3.11 -y
conda activate onescience311
pip install onescience[earth-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai

GPU Environment

conda create -n onescience311 python=3.11 -y libstdcxx-ng=12 libgcc-ng=12 gcc_linux-64=12 gxx_linux-64=12
conda activate onescience311
pip install onescience[earth-gpu] -i http://mirrors.onescience.ai:3141/pypi/simple/ --trusted-host mirrors.onescience.ai

Training Data Introduction

The paper uses SDO data from 2010 to 2024, including AIA 94, 131, 171, 193, 211, 304, 335, and 1600 Å, as well as HMI magnetic-field and Doppler-velocity products. The data are standardized to a 12-minute interval and spatially registered. The original training data are approximately 257 TB.

This model package uses synthetic data by default:

python scripts/fake_data.py

Each NPZ file contains at least inputs, targets, and activity. inputs has shape [N, 2, 13, H, W], targets has shape [N, S, 13, H, W], and activity stores the solar activity intensity at each forecast time step. When using real data, prepare files with the same fields and modify conf/config.yaml.

When using real data, do not run fake_data.py. Save temporally aligned, spatially registered, channel-organized, and signum-log normalized data to data/, replacing the synthetic data files:

data/train.npz
data/test.npz

Each NPZ file contains at least:

inputs:   float32 [N,2,13,H,W]
targets:  float32 [N,S,13,H,W]
activity: float32 [N,S]

The 13 channels correspond in order to eight AIA channels and five HMI products, and S is consistent with forecast_steps in conf/config.yaml. Modify conf/config.yaml according to the image size, number of forecast steps, channel statistics, and data paths of the real data. After data preparation, continue to use the unified training, inference, and evaluation commands below; use script arguments to override the default paths if other file locations are required.

Training

Single GPU:

python scripts/train.py

Multiple GPUs:

torchrun --nproc_per_node=8 --nnodes=1 --rdzv_id=1000 --rdzv_backend=c10d --max_restarts=0 --master_addr="localhost" --master_port=29500 scripts/train.py

Training outputs:

result/checkpoints/surya.pt
result/training/metrics.json

The training outputs include a model checkpoint for subsequent multi-step forecasting and training metrics that reflect different training stages, overall loss, and learning-rate changes, facilitating training-state preservation and model optimization analysis. Results on synthetic data are only for code-flow validation and do not represent paper results.

Training Weights

This repository provides weights trained on SDO/AIA and SDO/HMI solar observations in the weight/ folder. The weight files will be uploaded soon and are expected to be available in the near future.

Inference

python scripts/inference.py

Inference results are output to:

result/output/forecast.npz

Evaluation and Visualization

python scripts/result.py

Evaluation and visualization outputs are written to:

result/evaluation/metrics.json
result/evaluation/rollout_forecast_skill.png
result/evaluation/solar_dynamics_forecast.png
result/evaluation/solar_activity_evolution.png
result/evaluation/sdo_channel_error.png
result/evaluation/persistence_skill.png

The evaluation results comprehensively measure multi-step forecast errors, forecast skill relative to a persistence baseline, performance across AIA/HMI channels, and changes in solar activity intensity. Forecast images and lead-time curves show the evolution of solar dynamics and the long-term forecast stability of the model. The results follow the autoregressive forecasting protocol from the paper; results on synthetic data are only for validating the Surya workflow and do not represent performance on the paper's downstream tasks.

Official OneScience Resources

Platform	OneScience Main Repository	Skills Repository
Gitee	https://gitee.com/onescience-ai/onescience	https://gitee.com/onescience-ai/oneskills
GitHub	https://github.com/onescience-ai/OneScience	https://github.com/onescience-ai/oneskills

Citation and License

This repository is a reproduction of the original Surya paper.