基于物理引导深度学习的2公里分辨率逐小时气温不确定性感知制图
Uncertainty-Aware Hourly Air Temperature Mapping at 2 km Resolution via Physics-Guided Deep Learning
AI总结：
本文提出物理引导深度学习方法Amplifier Air-Transformer，通过重建云遮挡地表温度并转换为气温，结合不确定性估计，在美国本土实现2公里分辨率逐小时气温制图，验证精度达1.93摄氏度。
摘要：
近地表气温是地球表面的一个关键物理属性。尽管气象站提供连续监测，卫星提供广阔的空间覆盖，但没有任何单一数据源能以时空无缝的方式提供数据。在此，我们提出一种数据驱动的物理引导深度学习方法，以生成美国本土2公里分辨率的逐小时气温数据。该方法名为Amplifier Air-Transformer，首先重建被云层遮挡的GOES-16地表温度数据。它通过编码了年度温度循环的神经网络实现，包含一个线性项以在更精细尺度上放大ERA5温度值，以及卷积层以捕捉时空变化。然后，另一个神经网络通过利用其与关键地表属性的潜在关系，将重建的地表温度转换为气温。该方法通过深度集成学习的预测不确定性估计进一步增强，以提高可靠性。所提出的方法建立并测试于美国本土（2018-2024年）777亿个地表温度像素和1.55亿个气象站气温记录上，在站点验证中实现了1.93摄氏度的逐小时气温制图精度。该方法简化了地表温度重建和气温预测，并可扩展至其他卫星数据源，以实现高时空分辨率的无缝气温监测。本研究生成的数据可从https://doi.org/10.5281/zenodo.15252812下载，项目网页可在https://skrisliu.com/HourlyAirTemp2kmUSA/找到。
英文摘要：
Near-surface air temperature is a key physical property of the Earth's surface. Although weather stations offer continuous monitoring and satellites provide broad spatial coverage, no single data source offers seamless data in a spatiotemporal fashion. Here, we propose a data-driven, physics-guided deep learning approach to generate hourly air temperature data at 2 km resolution over the contiguous United States. The approach, called Amplifier Air-Transformer, first reconstructs GOES-16 surface temperature data obscured by clouds. It does so through a neural network encoded with the annual temperature cycle, incorporating a linear term to amplify ERA5 temperature values at finer scales and convolutional layers to capture spatiotemporal variations. Then, another neural network transforms the reconstructed surface temperature into air temperature by leveraging its latent relationship with key Earth surface properties. The approach is further enhanced with predictive uncertainty estimation through deep ensemble learning to improve reliability. The proposed approach is built and tested on 77.7 billion surface temperature pixels and 155 million air temperature records from weather stations across the contiguous United States (2018-2024), achieving hourly air temperature mapping accuracy of 1.93 C in station-based validation. The proposed approach streamlines surface temperature reconstruction and air temperature prediction, and it can be extended to other satellite sources for seamless air temperature monitoring at high spatiotemporal resolution. The generated data of this study can be downloaded at https://doi.org/10.5281/zenodo.15252812, and the project webpage can be found at https://skrisliu.com/HourlyAirTemp2kmUSA/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
