README.md

metadata

license: mit
tags:
  - heliophysics
  - solar_active_regions
  - AR_emergence
  - space_weather
  - machine_learning
size_categories:
  - 1GB<n<100GB

AR Emergence Dataset

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.

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:

• Acoustic power (from Doppler velocity maps)
• Photospheric magnetic field
• Continuum intensity

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.

These data products have already been applied successfully in machine learning models for AR emergence forecasting in [1].

[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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File Structure

The repository contains the following files and folders:

• data.zip — compressed folder containing all Active Region (AR) subfolders:

  • AR11130/
    • mean_int11130_flat.npz → continuum intensity timeline
    • mean_mag11130_flat.npz → magnetic field timeline
    • mean_pmdop11130_flat.npz → acoustic power timeline
  • AR11149/
  • …
  • AR13183/

• train.csv — training split (36 ARs)

• valid.csv — validation split (8 ARs)

• test.csv — test split (12 ARs)

• README.md — dataset description

Each AR folder contains three .npz files:

• mean_int{AR}_flat.npz → continuum intensity timeline
• mean_mag{AR}_flat.npz → magnetic field timeline
• mean_pmdop{AR}_flat.npz → 4 acoustic power timelines

The split CSV files (train.csv, valid.csv, test.csv) include:

Column	Description
AR	NOAA Active Region number
t_start	Start time of tracked patch
t_end	End time of tracked patch
dataset_type	train/valid/test split assignment
mean_int_path	Path to continuum intensity .npz file
mean_mag_path	Path to magnetic field .npz file
mean_pmdop_path	Path to acoustic power .npz file

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Example Usage

import numpy as np
import pandas as pd

# Load CSV metadata
df = pd.read_csv("train.csv")
print(df.head())

# Load one AR’s continuum intensity data
sample_path = df.iloc[0]["mean_int_path"]

# Update to local path after unzipping data.zip
sample_path = sample_path.replace("/Users/sk6617/Desktop/data", "data")  

data = np.load(sample_path)
print("Keys in npz file:", data.files)
print("Data shape:", data[data.files[0]].shape)