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--- |
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license: mit |
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tags: |
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- heliophysics |
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- solar_active_regions |
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- AR_emergence |
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- space_weather |
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- machine_learning |
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size_categories: |
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- 1GB<n<100GB |
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--- |
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# AR Emergence Dataset |
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The **AR Emergence Dataset** is designed to support research on the early detection of solar Active Regions (ARs) and the development of predictive models for space weather. By characterizing the evolution of ARs before, during, and after their emergence, the dataset enables studies of pre-emergence signatures and early warning methods. |
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This dataset is derived from NASA’s **Solar Dynamics Observatory (SDO)** using measurements from the **Helioseismic and Magnetic Imager (HMI)**. It includes timeline data of: |
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- **Acoustic power** (from Doppler velocity maps) |
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- **Photospheric magnetic field** |
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- **Continuum intensity** |
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for **56 large ARs** that emerged on the visible solar disk between **2010 and 2023**. Each AR is tracked within a **30° × 30° patch** over multiple days. |
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These data products have already been applied successfully in machine learning models for AR emergence forecasting in [1]. |
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[1] Kasapis, S., Kitiashvili, I. N., Kosovichev, A. G. & Stefan, J. T. Prediction of intensity variations associated with emerging active regions using helioseismic power maps and machine learning. The Astrophys. J. Suppl. Ser. 10.3847/1538-4365/adfbe2 (2025) |
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--- |
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## File Structure |
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The repository contains the following files and folders: |
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- `data.zip` — compressed folder containing all Active Region (AR) subfolders: |
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- `AR11130/` |
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- `mean_int11130_flat.npz` → continuum intensity timeline |
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- `mean_mag11130_flat.npz` → magnetic field timeline |
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- `mean_pmdop11130_flat.npz` → acoustic power timeline |
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- `AR11149/` |
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- … |
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- `AR13183/` |
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- `train.csv` — training split (36 ARs) |
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- `valid.csv` — validation split (8 ARs) |
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- `test.csv` — test split (12 ARs) |
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- `README.md` — dataset description |
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Each AR folder contains three `.npz` files: |
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- `mean_int{AR}_flat.npz` → continuum intensity timeline |
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- `mean_mag{AR}_flat.npz` → magnetic field timeline |
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- `mean_pmdop{AR}_flat.npz` → 4 acoustic power timelines |
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The split CSV files (`train.csv`, `valid.csv`, `test.csv`) include: |
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| Column | Description | |
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|----------------|-----------------------------------------------| |
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| `AR` | NOAA Active Region number | |
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| `t_start` | Start time of tracked patch | |
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| `t_end` | End time of tracked patch | |
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| `dataset_type` | train/valid/test split assignment | |
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| `mean_int_path`| Path to continuum intensity `.npz` file | |
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| `mean_mag_path`| Path to magnetic field `.npz` file | |
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| `mean_pmdop_path` | Path to acoustic power `.npz` file | |
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--- |
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## Example Usage |
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```python |
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import numpy as np |
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import pandas as pd |
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# Load CSV metadata |
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df = pd.read_csv("train.csv") |
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print(df.head()) |
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# Load one AR’s continuum intensity data |
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sample_path = df.iloc[0]["mean_int_path"] |
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# Update to local path after unzipping data.zip |
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sample_path = sample_path.replace("/Users/sk6617/Desktop/data", "data") |
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data = np.load(sample_path) |
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print("Keys in npz file:", data.files) |
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print("Data shape:", data[data.files[0]].shape) |
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