☀️ ✅ HelioQC

HelioQC is designed to support visual inspection of solar irradiance measurements, complementing automated quality-control (QC) tests. QC tests are essential for identifying measurements affected by instrumental or operational issues, and flagged measurements should be excluded from further analyses to prevent biases in applications such as model evaluation or development.

However, automated tests do not capture all anomalies. For example, some upper-limit tests may fail under cloudy conditions, as irradiance levels remain below the thresholds where these tests become effective. Therefore, the absence of QC flags does not guarantee that a measurement is correct.

In HelioQC, QC flags are treated as indicators of potential issues. A high frequency of flags should prompt further investigation, particularly through visual inspection. Although this step is often neglected due to being time-consuming and subjective, it is crucial for understanding anomalies and evaluating the true quality of the data.

To facilitate visual inspection, HelioQC provides a set of integrated visualizations in a multipanel layout, enabling the simultaneous interpretation of multiple diagnostics. This approach is based on the observation that different measurement issues can produce characteristic signatures across several visual representations. HelioQC thus offers a unified framework for QC data analysis, making the evaluation of solar irradiance measurements faster, more systematic, and more informative.

Example output

Setup

HelioQC is published as a Python package. Install in from from PyPI:

pip install helioqc

Usage

The HelioQC library provides both a Python API and Shell command (CLI).

The demonstration notebook provides detailed usage for both.

Citation

DOI: <TBD>

License

This software is released under the MIT license.

Author

Yves-Marie Saint-Drenan (yves-marie.saint-drenan@minesparis.psl.eu) | Mines Paris PSL | OIE