WeatherSyn: An Instruction Tuning MLLM For Weather Forecasting Report Generation

Accurate weather forecast reporting enables individuals and communities to better plan daily activities, agricultural operations, and transportation. However, the current reporting process primarily relies on manual analysis of multi-source data, which often leads to information overload and reduced efficiency. With the rapid advancement 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 WSInstruct, which covers 31 cities in America and 8 weather aspects. Based on this corpus, we develop the first model, WeatherSyn, specialized in generating weather forecast reports. Evaluation across multiple metrics on our dataset shows that WeatherSyn consistently outperforms leading closed-source MLLMs, particularly on structurally complex weather aspects. We further analyze its performance across diverse geographic regions and weather aspects. WeatherSyn demonstrates strong transferability across different regions, highlighting its zero-shot generalization capability. WeatherSyn offers valuable insight for developing MLLMs specialized in weather report generation.

This paper addresses the inefficiency of manual, multi-source weather reporting by introducing the Weather Forecasting Report (WFR) task and constructing the first instruction-tuning dataset, WSInstruct, which covers 31 U.S. cities across eight weather aspects. Based on this corpus, we develop WeatherSyn, the first model specialized for generating weather forecast reports. Experimental results show that WeatherSyn consistently outperforms leading closed-source multimodal large language models, especially on structurally complex weather aspects, while also demonstrating strong zero-shot generalization across different geographic regions. Overall, WeatherSyn provides valuable insights for developing data-driven MLLMs tailored to automatic weather report generation.