Back to AI Research

AI Research

SIREN: Towards End-to-End Extreme-Weather Early War... | AI Research

Key Takeaways

  • SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents addresses the limitations of current weather-forecasting systems,...
  • Early warning of extreme weather is essential for mitigating the societal, economic, and environmental risks posed by hazardous weather events.
  • However, expert-centered warning workflows are costly, labor-intensive, and difficult to scale throughout the warning-to-action process.
  • To bridge this gap, this study investigates automated end-to-end extreme-weather early warning through LLM agents.
  • We first develop SIREN-Bench, a comprehensive benchmark comprising 600 question-answer instances across 19 tasks, and covering four individual warning procedures and an end-to-end warning chain.
Paper AbstractExpand

Early warning of extreme weather is essential for mitigating the societal, economic, and environmental risks posed by hazardous weather events. However, expert-centered warning workflows are costly, labor-intensive, and difficult to scale throughout the warning-to-action process. Although recent advances in Large Language Model (LLM) agents have enabled the automation of weather-related tasks, existing studies remain centered on isolated scientific tasks and overlook the chain of interdependent processes required for operational extreme-weather early warning. To bridge this gap, this study investigates automated end-to-end extreme-weather early warning through LLM agents. We first develop SIREN-Bench, a comprehensive benchmark comprising 600 question-answer instances across 19 tasks, and covering four individual warning procedures and an end-to-end warning chain. Evaluation on SIREN-Bench reveals substantial capability gaps in existing weather agent frameworks. This motivates us to develop SIREN, an experience-grounded agent framework inspired by experts' use of historical cases, which combines an agentic execution environment integrating heterogeneous weather evidence and tools with a family of agent harnesses that exploit historical cases through retrieval, skill distillation, and predictive modeling. Extensive experiments demonstrate that SIREN outperforms weather-agent baselines on both individual warning procedures and end-to-end warning chains.

SIREN: Towards End-to-End Extreme-Weather Early Warning with Experience-Grounded LLM Agents addresses the limitations of current weather-forecasting systems, which often rely on manual, expert-driven workflows that are difficult to scale. While recent AI models have improved individual weather-related tasks, they often function in isolation and fail to manage the complex, multi-step chain required for operational early warning. This research introduces an automated, end-to-end framework that uses Large Language Model (LLM) agents to bridge the gap between initial hazard detection and final decision-making.

A New Benchmark for Operational Readiness

To evaluate how well AI agents perform in real-world scenarios, the researchers developed SIREN-Bench. This benchmark consists of 600 question-answer instances covering 12 types of extreme weather, such as tornadoes, floods, and heatwaves. Unlike previous benchmarks that focus only on early-stage forecasting, SIREN-Bench evaluates the entire warning-to-action pipeline. It tests agents across four critical procedures: characterizing the event, predicting its movement, assessing potential impacts, and making responsive decisions. Evaluations on this benchmark revealed significant performance gaps in existing AI frameworks, highlighting their lack of readiness for end-to-end operational use.

The SIREN Framework

The researchers created SIREN, an agent framework designed to mimic how human experts use historical experience to inform current decisions. The framework provides an "agentic environment" that integrates diverse data sources, including meteorological grids, satellite-style mesoanalysis images, and geospatial maps. By combining these tools with an executable code environment, the agents can perform complex analysis rather than relying solely on their internal training data.

Leveraging Historical Experience

A key innovation of SIREN is its ability to learn from the past. The framework includes a database of historical weather cases and uses several "harnesses" to apply this knowledge:

  • Retrieval: The agent finds similar past events to use as direct references for the current situation.

  • Skill Distillation: The agent reviews past successful solutions to learn and refine its own procedural skills.

  • Predictive Modeling: The agent uses historical data to build and validate task-specific machine learning models to improve its downstream analysis.

Performance and Impact

Extensive experiments show that SIREN consistently outperforms existing weather-agent baselines. By grounding its reasoning in both real-time evidence and historical experience, the framework demonstrates superior capability in handling both individual warning procedures and the complex, interdependent tasks required for an end-to-end early warning system. This approach moves the field closer to a fully automated, scalable, and reliable system for mitigating the risks posed by extreme weather.

Comments (0)

No comments yet

Be the first to share your thoughts!