IonoBench: Evaluating Spatiotemporal Models for Ionospheric Forecasting under Solar-Balanced and Storm-Aware Conditions
Published in Remote Sensing (MDPI)

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This project is a benchmark framework for evaluating deep spatiotemporal models on Global Ionospheric Map (GIM) forecasting. The framework provides standardized datasets, evaluation protocols, pretrained models, and configuration-based experimentation.

Overall Performance on IonoBench Test Set

Model	RMSE (↓)	R² (↑)	SSIM (↑)
SimVPv2	2.25 ± 1.35	0.962 ± 0.015	0.969 ± 0.020
DCNN121	2.62 ± 1.66	0.950 ± 0.023	0.963 ± 0.025
SwinLSTM	2.66 ± 1.49	0.946 ± 0.020	0.960 ± 0.023
IRI 2020	6.39 ± 4.53	0.720 ± 0.109	0.852 ± 0.043

Click Open in Colab to test without local setup.

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Features

• Supports multichannel spatiotemporal models for multistep 24-hour input → 24-hour output setup
• Stratified and chronological datasets (Preprocessed GIMs and auxiliary parameters)
• Model registry and configuration system
• Pretrained model download via Hugging Face
• Solar-balanced and storm-aware evaluation experiments
• Tutorials for Colab and local setups.

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Framework Status

Component	Status
HF model & data access	Complete
Model/config registry	Complete
Evaluation pipeline	Complete
Visualization tools	Complete
CLI support	In Progress
Training tutorials	Planned
Contributor guide	Planned

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Local Setup

# Clone repository
git clone https://github.com/Mert-chan/IonoBench.git

# Change your directory
cd IonoBench

# Create environment
conda create -n ionobench python=3.11 -y
conda activate ionobench

# Install dependencies
pip install -r requirements.txt

Tested on: Python 3.11.13 · PyTorch 2.5.1 · CUDA 12.4
The environment uses torch==2.5.1, which requires a compatible CUDA build.
PyTorch provides separate wheels for each CUDA version (e.g., +cu118, +cu121, +cu124).
Ensure your NVIDIA driver supports the CUDA version used in the installed wheel.

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How to Cite

If you use IonoBench in your research, please cite:

Turkmen, M.C.; Lee, Y.H.; Tan, E.L.
IonoBench: Evaluating Spatiotemporal Models for Ionospheric Forecasting Under Solar-Balanced and Storm-Aware Conditions.
Remote Sensing, 2025, 17(15), 2557. https://doi.org/10.3390/rs17152557

BibTeX:

@article{Ionobench2025,
  title   = {IonoBench: Evaluating Spatiotemporal Models for Ionospheric Forecasting under Solar-Balanced and Storm-Aware Conditions},
  author  = {Turkmen, M.C.; Lee, Y.H.; Tan, E.L.},
  journal = {Remote Sensing},
  year    = {2025},
  volume  = {17},
  number  = {15},
  pages   = {2557},
  doi     = {10.3390/rs17152557}
}