发表机构
Ocean University of China（中国海洋大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种结合文本环境上下文与空间图的LLM方法，用于区域多步SST预测，通过条件数值生成和双图神经网络注入空间状态，在南中国海取得最优MAE和R²，并提供事后解释。
AI 中文摘要
海表温度（SST）预测依赖于局部时间持续性、区域空间依赖性以及随预测日期演变的环境条件。我们研究了如何在不将完整SST网格序列化为文本的情况下，将这些异构条件呈现给大语言模型（LLM）以进行区域多步预测。我们将预测建模为条件数值生成：历史SST和异常序列、日期对齐的环境记录以及静态海洋知识构成文本上下文，而区域空间状态则通过连续的图派生前缀提供。静态图编码了持久的地理-气候关系，动态图编码了近期SST相关性和局部热带气旋影响。两个图神经网络产生目标节点表示，该表示通过空间前缀融合映射并注入LLM输入。在南中国海的SST预测中，完整配置在十个预测步骤中取得了与对比方法相比最佳的MAE和R²。在数值预测的同时，一个基于规则的模块将预测趋势和环境因子方向与知识条目匹配，返回带来源链接的事后上下文解释。
英文摘要
Sea surface temperature (SST) forecasting depends on local temporal persistence, regional spatial dependence, and environmental conditions that evolve with the forecast date. We study how these heterogeneous conditions can be presented to a large language model (LLM) for regional multi-step forecasting without serializing the full SST grid as text. We formulate forecasting as conditional numerical generation: historical SST and anomaly sequences, date-aligned environmental records, and static ocean knowledge form a textual context, while regional spatial state is supplied through continuous graph-derived prefixes. A static graph encodes persistent geographic--climatological relations, and a dynamic graph encodes recent SST correlations and localized tropical-cyclone influence. Two graph neural networks produce a target-node representation that is mapped by a spatial-prefix fusion and injected into the LLM input. On SST forecasting in the South China Sea, the complete configuration achieves the best MAE and $\Rtwo$ among the compared methods over ten forecast steps. Alongside the numerical forecast, a rule-based module matches predicted trends and environmental-factor directions with knowledge entries to return source-linked, post-hoc contextual explanations.
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