Turning Weather Data into Plain English

by

Author: Denis Avetisyan

A new AI system translates complex numerical forecasts into human-understandable reports, complete with explanations and contextual awareness.

The system generates comprehensive meteorological reports integrating detailed multi-parameter forecasts with time-series visualizations of critical weather variables, offering a holistic view of anticipated conditions.
The system generates comprehensive meteorological reports integrating detailed multi-parameter forecasts with time-series visualizations of critical weather variables, offering a holistic view of anticipated conditions.

This paper presents AI-Meteorologist, a modular system leveraging large language model agents for transparent and explainable weather interpretation using time-series reasoning and climatological context.

While weather forecasting increasingly relies on complex numerical models, translating these data into readily understandable and scientifically grounded narratives remains a significant challenge. This paper introduces ‘A Modular LLM-Agent System for Transparent Multi-Parameter Weather Interpretation’-an explainable AI framework, AI-Meteorologist, that converts raw forecasts into coherent reports detailing atmospheric drivers and localized dynamics. By employing an agent-based system leveraging time-series reasoning and climatological context-all without requiring model fine-tuning-we demonstrate a pathway toward truly interpretable weather analysis. Could this approach not only enhance meteorological expertise but also unlock new avenues for scientific discovery in climate analytics?

Beyond the Black Box: Why We Need to Understand, Not Just Predict, the Weather

Conventional weather prediction hinges on intricate numerical models-sophisticated systems of equations simulating atmospheric behavior. However, these models often function as “black boxes,” delivering forecasts without readily explaining why a particular outcome is predicted. This lack of interpretability hinders effective communication of forecast information, particularly for decision-makers who require understanding, not just numbers. Moreover, while these models excel at calculating a single, most likely scenario, they frequently struggle to adequately convey the inherent uncertainty in weather prediction. This is problematic because even highly refined models are susceptible to errors, and failing to communicate the range of possible outcomes can lead to underpreparedness for unexpected events or miscalculated risks. Consequently, there is growing demand for forecasting systems that prioritize both accuracy and transparent communication of potential forecast variability.

The escalating incidence of extreme weather necessitates a shift from simply forecasting what will happen to understanding how these events will impact communities and infrastructure. Traditional predictive models, while improving in accuracy, often fall short in translating raw data into readily usable intelligence for emergency management and public safety. A crucial advancement lies in developing systems capable of assessing potential hazards – factoring in vulnerability, exposure, and the cascading effects of a disaster – to deliver actionable insights. This means moving beyond alerts about approaching storms to providing specific, localized risk assessments, informing targeted evacuations, resource allocation, and proactive mitigation strategies. Ultimately, the focus is no longer solely on prediction, but on empowering decision-makers with the knowledge needed to minimize damage and protect lives in an era of increasingly frequent and intense weather events.

Contemporary weather analysis frequently operates with a fragmented approach to data integration, hindering the delivery of truly insightful forecasts. Existing systems typically process raw forecast outputs alongside historical climatological data, but often fail to synthesize these diverse inputs into a unified, easily digestible format for decision-makers. This limitation is further compounded by a reliance on hourly resolution, restricting analysis to a relatively short timeframe of approximately five days. Consequently, crucial long-term trends or the potential for compounding extreme events may be overlooked, diminishing the utility of forecasts for proactive hazard mitigation and resource allocation. A shift towards holistic data fusion and multi-scale analysis is therefore essential to unlock the full potential of available information and provide more comprehensive, actionable weather intelligence.

The weather report generation process integrates observational data with a predictive model to produce a comprehensive forecast.
The weather report generation process integrates observational data with a predictive model to produce a comprehensive forecast.

AI-Meteorologist: Deconstructing Complexity with Modular Design

AI-Meteorologist employs a modular, agent-based architecture built upon Large Language Model (LLM) Agent Systems to convert numerical weather prediction data into easily understandable reports. This approach decomposes the task of weather intelligence into distinct agent roles, facilitating specialized processing and improved accuracy. The system isn’t a monolithic application; rather, it’s comprised of independent modules that communicate and collaborate to achieve the final output. By leveraging LLM-Agent Systems, AI-Meteorologist can interpret complex data sets, identify relevant patterns, and generate coherent, human-readable summaries of current and forecasted weather conditions, moving beyond raw data presentation to deliver actionable insights.

The AI-Meteorologist system ingests weather data via external APIs, specifically the OpenWeather One Call 2.5 API and the Meteostat API, to construct a comprehensive environmental profile. These APIs provide access to over ten distinct meteorological parameters, including but not limited to: current temperature readings in Celsius or Fahrenheit, percentage humidity, wind speed and direction, various precipitation types and amounts, visibility distances, atmospheric pressure, and cloud cover. Historical data, also sourced via these APIs, allows for trend analysis and improved forecast contextualization. Data is retrieved in standardized formats, typically JSON, enabling efficient parsing and integration into the agent-based system for subsequent analysis and report generation.

The AI-Meteorologist system is architected around two primary agents: the Meteorologist Agent and the Writer Agent. The Meteorologist Agent is responsible for data acquisition from APIs like OpenWeather One Call 2.5 and Meteostat, followed by the analysis of over ten weather parameters – including temperature, humidity, wind speed, precipitation type and intensity, and visibility – to identify significant weather patterns and potential hazards. The output of the Meteorologist Agent, consisting of analyzed data and derived insights, is then passed to the Writer Agent. The Writer Agent structures this information into a human-readable weather report, focusing on clarity and conciseness, and ensuring the final output is logically organized and easily understandable for end-users.

Weather reports are generated through the coordinated interaction of core workflow modules, including weather APIs, file management, visualization, and end-to-end report creation.
Weather reports are generated through the coordinated interaction of core workflow modules, including weather APIs, file management, visualization, and end-to-end report creation.

Context is King: How Spatial Awareness and Temporal Reasoning Refine Forecasts

The Meteorologist Agent utilizes time-series reasoning by analyzing historical and current weather data to predict future conditions, incorporating variables such as temperature, precipitation, wind speed, and atmospheric pressure. This temporal analysis is coupled with geospatial metadata – location-based information including latitude, longitude, elevation, and land cover type – to refine forecasts for specific geographic areas. By integrating these data types, the agent can model how weather patterns evolve over time and vary across different locations, providing a nuanced understanding of localized weather impacts. This approach allows for the generation of forecasts that are not simply predictions of broad conditions, but rather detailed assessments of weather effects at a granular level, accounting for regional characteristics and temporal trends.

Integration of OpenStreetMap (OSM) data significantly refines forecast accuracy by incorporating detailed regional characteristics. OSM provides data delineating land use, building density, and surface types – differentiating between urban, suburban, and rural environments. This allows the system to model the impact of these features on local weather conditions; for example, the urban heat island effect or the differing rates of evaporation between paved and vegetated surfaces. By factoring in these region-specific details, forecasts account for localized variations not captured by broader meteorological models, leading to more precise predictions of temperature, precipitation, and wind patterns at a granular level.

The system utilizes real-time data analysis to identify approaching cold fronts and deviations from typical precipitation patterns, facilitating proactive hazard assessments. This is achieved by comparing current meteorological data against a 20-year historical average sourced from the Meteostat API, enabling the detection of anomalous precipitation events. Identified anomalies, coupled with cold front detection, trigger hazard reasoning processes to predict potential impacts and provide early warnings. This comparative analysis allows for the differentiation between normal weather fluctuations and potentially hazardous conditions, improving the accuracy and timeliness of alerts.

Beyond Prediction: Delivering Transparent Insights for Informed Decision-Making

AI-Meteorologist distinguishes itself through a commitment to Explainable AI (XAI), moving beyond the “black box” predictions common in many artificial intelligence systems. The system doesn’t simply output a forecast; it provides a clear rationale, detailing which atmospheric variables – temperature, pressure, humidity, wind speed, and others – most heavily influenced the prediction. This transparency is achieved through techniques that allow examination of the model’s internal logic, revealing the relative importance of each input feature. Crucially, this process isn’t opaque; the reasoning is designed to be reproducible, meaning that given the same inputs, the system will consistently arrive at the same forecast and, importantly, offer the same explanation. This focus on clarity builds user trust and allows for effective validation, as experts can assess the soundness of the AI’s reasoning, ultimately improving the reliability and accuracy of weather predictions.

The AI-Meteorologist system employs an Illustrator Agent to translate raw forecast data into readily understandable visuals. Utilizing the versatile matplotlib library, this agent doesn’t simply present numbers; it constructs compelling charts, graphs, and maps that highlight key weather patterns and potential impacts. This visual approach is crucial because complex meteorological data – encompassing variables like temperature gradients, wind vectors, and precipitation probabilities – can overwhelm a user when presented solely as text or tables. By transforming these datasets into intuitive imagery, the Illustrator Agent enables quicker comprehension and more effective decision-making, allowing individuals and organizations to easily grasp the forecasted conditions and their potential consequences. The agent’s output isn’t limited to static images either; it can dynamically generate visualizations that update alongside the evolving forecast, providing a continuously refreshed and accessible understanding of the weather.

AI-Meteorologist transcends simple prediction by delivering weather insights designed to facilitate proactive decision-making. The system doesn’t merely forecast what will happen, but clarifies why, leveraging Explainable AI to reveal the data and logic driving each prediction. This is coupled with the generation of intuitive visualizations, transforming complex meteorological data into easily understandable formats. Consequently, users – from individual citizens planning daily activities to professionals managing critical infrastructure – gain a powerful tool for assessing risk, optimizing resource allocation, and ultimately, responding effectively to weather events. The confluence of data-driven accuracy, transparent reasoning, and accessible visuals establishes AI-Meteorologist as a platform for informed preparedness and resilience.

The pursuit of elegantly modular systems, as demonstrated by AI-Meteorologist, invariably courts eventual complexity. This paper attempts to bridge the gap between raw numerical forecasts and human understanding through agent-based reasoning-a commendable effort, yet one destined to accumulate its own form of technical debt. As Blaise Pascal observed, “The eloquence of youth is that it knows nothing.” This system, while promising in its ability to provide interpretable weather reports, will inevitably face the brutal test of production realities. Better a single, thoroughly understood forecasting model than a constellation of agents each masking unforeseen interactions. The elegance of the design belies the inevitable entropy that awaits.

The Inevitable Rot

This ‘AI-Meteorologist’ – a modular system translating numbers into plausible narratives – feels less like a breakthrough and more like a beautifully-packaged extension of existing technical debt. The claim of avoiding specialized fine-tuning is, of course, optimistic. Any system exposed to production data will eventually require it, not to improve accuracy, but to explain why it’s wrong in increasingly subtle ways. The elegance of modularity will be tested not by the model’s initial performance, but by the cost of untangling its dependencies when some unforeseen atmospheric phenomenon reveals a fundamental flaw.

The emphasis on ‘explainable AI’ is particularly amusing. Humans crave explanations when systems fail, not when they succeed. A verbose justification for an incorrect forecast doesn’t inspire confidence; it simply provides more material for post-mortem analysis. And let’s be clear: anything self-healing just hasn’t broken yet. The real challenge isn’t generating natural language; it’s accepting that some errors are irreducible, inherent to the chaotic nature of the system being modeled.

Future work will inevitably focus on scaling – more parameters, more data, more elaborate reasoning chains. But a truly robust system might instead prioritize simplicity. If a bug is reproducible, it confirms a stable system, not a flawed one. Perhaps the next generation of weather AI will be less concerned with mimicking human expertise and more focused on reliably identifying the limits of its own understanding.

Original article: https://arxiv.org/pdf/2512.11819.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-16 12:20