Explaining Weather Bulletins via ILP

Weather forecasting often relies on complex, "black-box" deep learning models that provide high accuracy but lack transparency. This makes it difficult for human experts to understand the rationale behind specific forecast outputs. The authors investigate whether Inductive Logic Programming (ILP) can bridge this gap by learning human-readable, logical rules that explain the decisions made in official weather bulletins.

The researchers built a pipeline called SERGIO (Simulating Explanation of ReGIOnal weather forecast) to process meteorological data. The process involves three main steps:

1. Data Extraction: Raw meteorological data from the CERRA reanalysis dataset (covering cloud cover, humidity, temperature, and wind) is converted into Answer Set Programming (ASP) facts.
2. Ground Truth Generation: The team uses multi-scale template matching on historical weather bulletins to extract the "ground truth"—the specific symbols and precipitation levels chosen by human experts for different geographic locations.
3. Rule Induction: Using the FastLAS2 framework, the system searches for optimal logical rules that correlate the meteorological input facts with the expert-chosen bulletin symbols. These rules are designed to be interpretable, effectively acting as a surrogate model that explains the experts' reasoning.

Findings and Significance

The study demonstrates that ILP can successfully derive logical hypotheses that explain the rationale behind weather pictograms. By moving from black-box predictions to symbolic rules, the approach provides a path toward "explainable AI" in meteorology. This is significant because it enhances trust in automated systems and allows domain experts to validate the logic behind forecasts, rather than simply accepting model outputs at face value. The methodology is designed to be generalizable, meaning it could be applied to different regions or meteorological sources beyond the initial test case in Friuli Venezia-Giulia.