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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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- n<1K |
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configs: |
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- config_name: default |
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default: true |
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data_files: |
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- split: train |
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path: train.csv |
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- split: validation |
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path: validation.csv |
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- split: test |
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path: test.csv |
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pretty_name: Active Region Emergenc |
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--- |
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# Active Region Emergence Dataset |
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## Dataset Summary |
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The **Active Region 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 **59 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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## Supported Tasks and Applications |
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- **Spatio-temporal Forecasting**: Forecasting the spatially resolved emergence characteristics of active regions. |
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## Data Structure |
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### Data Files |
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The repository contains csv files containing paths and compressed data files for emergence characteristics. |
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The dataset is split for consistent training, validation and testing. |
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- `train.csv` — training split (37 ARs) |
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- `valid.csv` — validation split (9 ARs) |
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- `test.csv` — test split (13 ARs) |
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- `README.md` — dataset description |
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- `data.zip` — compressed folder containing all Active Region (AR) subfolders: |
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- `AR{noaa_num}/` |
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- `mean_int{noaa_num}_flat.npz` → continuum intensity timeline |
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- `mean_mag{noaa_num}_flat.npz` → magnetic field timeline |
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- `mean_pmdop{noaa_num}_flat.npz` → 4 acoustic power timeline (2–3, 3–4, 4–5, 5–6 mHz). |
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- Index 0: 2-3 mHz |
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- Index 1: 3-4 mHz |
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- Index 2: 4-5 mHz |
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- Index 3: 5-6 mHz |
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where `noaa_num` is the NOAA Active Region Number for the active regions in this dataset. |
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### Features |
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CSV files include following features: |
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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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- **`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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## Dataset Details |
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| Field | Description | |
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|------------------------|---------------------------------------------| |
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| **Temporal Coverage** | 2010 – 2023 | |
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| **Data Format** | CSV for metadata / NPZ for rasters | |
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| **Data Size** | Total 59 instances | |
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| **Total File Size** | ~23.1 MB | |
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| **Data Shape** | (6, 240, 9, 9) per instance | |
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| **Cadence** | 1 hour | |
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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 power map dopplergram data |
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# for intensity and magnetic flux use mean_int_path and mean_mag_path column names on df |
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sample_path = df.iloc[0]["mean_pmdop_path"] |
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# Update to local path after unzipping data.zip |
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sample_path = sample_path.replace("/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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``` |
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## Contact |
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[1] Spyros Kasapis [[email protected]](mailto:[email protected]) |
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## References |
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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) |
