发表机构
Nanjing University of Information Science and Technology; Nanyang Technological University; Harbin Institute of Technology; Princeton University（南京信息工程大学; 南洋理工大学; 哈尔滨工业大学; 普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FlexCast提出一种场自适应天气预报模型，通过元数据条件适配器和掩码集融合处理任意字段集，在69字段ERA5注册表上实现跨配置预测，实验验证了其有效性与上下文匹配的重要性。
AI 中文摘要
大多数深度学习天气模型将一组固定的变量和气压层分配给预定义的通道，这限制了模型在不同大气场配置之间的迁移能力。这种对固定字段集的依赖限制了训练好的模型在不同大气场配置之间的可迁移性。我们提出了FlexCast，一种场自适应天气预报模型，它使用一组参数为从69字段ERA5注册表中抽取的可变基数子集生成身份对齐的预测。具体来说，一个元数据条件适配器首先编码变量身份、气压层和字段类型，并将它们与空间特征结合。然后，共享的秩为16的投影由元数据依赖的门控调制，以产生特定于字段的特征，而掩码集融合将可用字段聚合为固定宽度的表示。随后，一个多尺度U-Transformer处理融合的大气特征，而身份感知的查询解码器为请求的字段生成预测。最后，FlexCast学习标准化的六小时增量，并递归应用它以生成更长提前期的预测。在2020年ERA5测试集上的实验表明，FlexCast能够在不同的字段配置下运行。兼容的跨字段上下文与较低的预测误差相关，而不匹配的上下文则会增加误差。
英文摘要
Most deep learning weather models assign a fixed set of variables and pressure levels to predefined channels, limiting transfer across atmospheric field configurations. This dependence on a fixed field set limits the transferability of trained models across atmospheric field configurations. We propose FlexCast, a field-adaptive weather forecasting model that uses a single set of parameters to produce identity-aligned forecasts for variable-cardinality subsets drawn from a 69-field ERA5 registry. Specifically, a metadata-conditioned adapter the first encodes variable identity, pressure level, and field type and combines them with spatial features. Then, shared rank-16 projec?tions are modulated by metadata-dependent gates to produce field?specific features, while masked set fusion aggregates the available fields into a fixed-width representation. Subsequently, a multiscale U-Transformer processes the fused atmospheric features, while an identity-aware query decoder produces forecasts for the requested fields. Finally, FlexCast learns a standardized six-hour increment and applies it recursively to generate forecasts at longer lead times. Experiments on the 2020 ERA5 test set demonstrate that FlexCast operates across varying field configurations. Compatible cross-field context is associated with lower forecast errors, whereas mismatched context increases them.
Comments5 pages, 2 figures, 4 tables
