发表机构
Montana State University; Imam Khomeini International University（蒙大拿州立大学; 伊玛目霍梅尼国际大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估物理信息神经网络在稀疏观测下的大气温度短期预报，发现物理约束的效益随预报时效增长、在严重观测稀疏下保持并跨区域迁移，但受限于复杂地形下的固定垂直坐标表示。
AI 中文摘要
短期大气温度预报是支撑气候感知数字孪生系统所必需的，但此类预报必须在热力观测不完整的情况下生成。本研究评估了一种受物理信息约束的神经网络用于位温预报，该网络受气压坐标系下的热力平流-源方程约束，并采用由前12小时时段拟合且在后续训练前冻结的绝热源闭合方案。利用三个气压层的逐小时ERA5再分析数据，该模型作为条件后报在1、2和3小时的提前期下，与持续性预报、局部趋势预报以及两个匹配的神经网络基线进行对比评估，其中一个基线接收与PINN相同的未来气象强迫，有助于区分物理约束与未来强迫信息的作用。在俄克拉荷马州的开发案例中，相对于最强基线的平均均方根误差改进从1小时的8.1%增长到3小时的23.8%；在观测密度扫描低至候选位置5%的情况下，这一3小时优势保持在14.6%至16.9%之间，且没有证据表明更低密度能提升性能。在将固定协议迁移至阿拉巴马州一次热事件并采用三种虚拟观测布局的实验中，3小时改进幅度为19.7%至24.4%，且各层级均获得一致改进。一项平行的蒙大拿州压力测试中，固定气压层与复杂地形相交，导致3小时性能下降约17.5%，揭示了该公式在地形相关的适用性限制。综合这些结果表明，物理约束的效益随预报时效增长，在严重观测稀疏条件下依然保持，并可在不同区域间迁移，但其受限于固定垂直坐标表示在复杂地形上的有效性，这一证据对气候感知预报和数字孪生系统中物理约束组件的设计具有参考意义。
英文摘要
Short-horizon forecasts of atmospheric temperature are needed to support climate-aware digital-twin systems, but such forecasts must be produced where thermal observations are incomplete. This study evaluates a physics-informed neural network for potential-temperature forecasting, constrained by a pressure-coordinate thermodynamic advection-source equation and a diabatic-source closure fit from the preceding 12-hour period and frozen before future-time training. Using hourly ERA5 reanalysis at three pressure levels, the model is evaluated as a conditional hindcast at lead times of one, two and three hours against persistence, local-trend, and two matched neural-network baselines, one of which receives the same future meteorological forcing as the PINN, helping distinguish the physical constraint from access to future forcing. In an Oklahoma development case, mean RMSE improvement over the strongest baseline grew from 8.1\% at one hour to 23.8\% at three hours; under an observation-density sweep down to 5\% of candidate locations, this 3-hour advantage remained 14.6--16.9\%, with no evidence that lower density improves performance. Under a fixed protocol transferred to an Alabama heat event with three virtual-observation layouts, three-hour improvement ranged 19.7-24.4\% with consistent origin-level wins. A parallel Montana stress test, in which fixed pressure levels intersected complex terrain, produced a three-hour degradation of roughly 17.5\%, identifying a terrain-related applicability limit of the formulation. Together, these results indicate that the physics constraint's benefit grows with forecast horizon, persists under severe observation sparsity, and transfers across regions, but is bounded by the validity of a fixed vertical-coordinate representation over complex terrain, evidence relevant to physics-constrained components of climate-aware forecasting and digital-twin systems.
