A Modular LLM-Agent System for Transparent Multi-Parameter Weather Interpretation

TL;DR

This paper presents AI-Meteorologist, an explainable LLM-based system that transforms raw weather forecasts into scientifically grounded, transparent narrative reports, enhancing interpretability and supporting meteorological analysis without model fine-tuning.

Contribution

The paper introduces a novel agent-based LLM framework for weather interpretation that provides transparent reasoning and explanations directly from in-context prompts, without requiring fine-tuning.

Findings

Generates structured weather explanations with transparent reasoning.

Effectively identifies weather fronts, anomalies, and local dynamics.

Demonstrates interpretability through case studies on multi-location data.

Abstract

Weather forecasting is not only a predictive task but an interpretive scientific process requiring explanation, contextualization, and hypothesis generation. This paper introduces AI-Meteorologist, an explainable LLM-agent framework that converts raw numerical forecasts into scientifically grounded narrative reports with transparent reasoning steps. Unlike conventional forecast outputs presented as dense tables or unstructured time series, our system performs agent-based analysis across multiple meteorological variables, integrates historical climatological context, and generates structured explanations that identify weather fronts, anomalies, and localized dynamics. The architecture relies entirely on in-context prompting, without fine-tuning, demonstrating that interpretability can be achieved through reasoning rather than parameter updates. Through case studies on multi-location forecast data, we show how AI-Meteorologist not only communicates weather events but also reveals the underlying atmospheric drivers, offering a pathway toward AI systems that augment human meteorological expertise and support scientific discovery in climate analytics.