首个专用于天气预报生成的多模态大模型，自动写报告更准更快。
WeatherSyn: An Instruction Tuning MLLM For Weather Forecasting Report Generation
- 用31个城市8类天气数据训练指令微调模型
- 在复杂天气结构上超越主流闭源模型表现
- 跨区域零样本泛化强，适合气象机构用
精准的天气预报报告有助于个人和社区更好地规划日常活动与农事。然而，当前报告仍主要依赖人工分析多源数据，易造成信息过载、效率低下。随着多模态大语言模型（MLLM）的发展，利用数据驱动模型在气象领域进行分析与报告生成仍处于探索阶段。本文提出天气预报报告（WFR）任务，并构建首个面向该任务的指令微调数据集 \ exttt{WeatherSyn}，涵盖美国31个城市及8类天气要素。基于此数据集，我们开发首个专注天气预报报告生成的模型 \ exttt{WeatherSyn}。在自建数据集上的多指标评估显示，\ exttt{WeatherSyn} 持续优于领先闭源MLLM，尤其在结构复杂的天气要素上表现突出。进一步分析表明，该模型在不同地理区域与天气类型间具备强迁移能力，展现优异的零样本泛化性能，为气象领域专用MLLM研发提供重要参考。
原文摘要 · Abstract (English)
Accurate weather forecast reporting enables individuals and communities to better plan daily activities and agricultural operations. However, the current reporting process primarily relies on manual analysis of multi-source data, which leads to information overload and reduced efficiency. With the development of multimodal large language models (MLLMs), leveraging data-driven models to analyze and generate reports in the weather forecasting domain remains largely underexplored. In this work, we propose the Weather Forecasting Report (WFR) task and construct the first instruction-tuning dataset for this task, named~\DatasetNameL, which covers 31 cities in America and 8 weather aspects. Based on this corpus, we develop the first model, \ModelNameL, specialized in generating weather forecast reports. Evaluation across multiple metrics on our dataset shows that \ModelNameL~ consistently outperforms leading closed-source MLLMs, particularly on structurally complex weather aspects. We further analyze its performance across diverse geographic regions and weather aspects. \ModelNameL~ demonstrates strong transferability across different regions, highlighting its zero-shot generalization capability. \ModelNameL~offers valuable insight for developing MLLMs specialized in weather report generation. .
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