发表机构
Southeast University; Fudan University; Sun Yat-sen University; Tsinghua University（东南大学; 复旦大学; 中山大学; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对现有区域温度预报器输出固定的问题，提出CSTF模型，通过引入提前期、分辨率等查询实现灵活的温度预报，在基准测试中降低了17.0%的偏差，表现出更优性能。
AI 中文摘要
准确的区域近地面温度预报是短期气象服务及下游风险评估的基础。现有基于深度学习的区域预报器通常在规定网格上生成固定数量的未来帧，这限制了其在预报产品需按查询相关的提前期或显示分辨率进行评估时的应用。为克服这些固定输出约束，我们将区域2米温度（T2M）预报建模为查询条件下的连续时空温度场评估，并提出连续时空温度预报器（Continuous Spatiotemporal Temperature Forecaster, CSTF），这是一种神经场，在评估2米温度（T2M）时将预报提前期和输出分辨率转化为显式查询。具体而言，CSTF首先将多变量ERA5历史数据编码为潜在气象状态，随后将T2M解码为基于坐标的场。据此，空间位置、预报提前期和输出分辨率被作为查询引入，使得在统一的场评估框架内可实现标准逐小时预报、中间提前期诊断及分辨率可控的输出。此外，为维持灵活场查询间的一致性，我们设计了空间梯度、时间差异及尺度一致性目标，以正则化区域热力结构、逐提前期演化及跨分辨率一致性。在中国东南部0-6小时ERA5-Land基准上的实验表明，CSTF实现了最佳的总体确定性技能，包括偏差降低17.0%，全局范围诊断进一步展示了灵活的提前期及分辨率可控推理。
英文摘要
Accurate regional near-surface temperature forecasting is fundamental to short-range weather services and downstream risk assessment. Existing deep learning-based regional forecasters commonly produce a fixed set of future frames on a prescribed grid, limiting their use when forecast products must be evaluated at query-dependent lead times or display resolutions. To overcome these fixed-output constraints, we formulate regional T2M forecasting as query-conditioned continuous spatiotemporal temperature field evaluation and propose the Continuous Spatiotemporal Temperature Forecaster (CSTF), a neural field that turns forecast lead time and output resolution into explicit queries when evaluating 2-m temperature (T2M). Specifically, CSTF first encodes multivariable ERA5 histories into latent meteorological states and then decodes T2M as a coordinate-based field. Accordingly, spatial location, forecast lead time, and output resolution are introduced as queries, enabling standard hourly forecasts, intermediate lead-time diagnostics, and resolution-controllable outputs within a unified field-evaluation framework. Furthermore, to maintain coherence across flexible field queries, we design spatial-gradient, temporal-difference, and scale-consistency objectives that regularize regional thermal structures, lead-wise evolution, and cross-resolution agreement. Experiments on the Southeast China 0-6 h ERA5-Land benchmark demonstrate that CSTF achieves the best aggregate deterministic skill, including a 17.0 percent reduction in Bias, with global-scope diagnostics further illustrating flexible lead-time and resolution-controllable inference.
Comments16 pages, 15 figures
