用地面相机重建高精度四维云状态，实现分钟级、25米分辨率的云量追踪。
Cloud4D: Estimating Cloud Properties at a High Spatial and Temporal Resolution
- 基于同步地基相机与自适应变换器，从二维图像推断三维液态水分布。
- 空间分辨率25米、时间分辨率5秒，较卫星提升一个数量级。
- 适用于气象监测、短时强降水预测，适合气象与遥感研究者使用。
机器学习推动了数值天气预报与气候模型的发展，但多数全球模型空间分辨率仅达千米级，难以刻画单个云体及极端降水、阵风、湍流和地表辐照等现象。因此亟需更高分辨率模型，而这对高分辨率真实观测提出挑战，现有仪器难以满足。本文提出Cloud4D，首个仅依赖同步地基相机的基于学习的四维云状态重建框架。通过采用仿射引导的2D到3D变换器，系统在25米空间分辨率和5秒时间分辨率下推断出液态水含量的完整三维分布。通过跟踪三维液态水含量随时间变化，进一步估计水平风矢量。在为期两个月、六台朝天相机部署中，系统相较最先进卫星测量实现数量级的空间-时间分辨率提升，且与共位雷达测量相比相对误差低于10%。代码与数据见项目主页 https://cloud4d.jacob-lin.com/。
原文摘要 · Abstract (English)
There has been great progress in improving numerical weather prediction and climate models using machine learning. However, most global models act at a kilometer-scale, making it challenging to model individual clouds and factors such as extreme precipitation, wind gusts, turbulence, and surface irradiance. Therefore, there is a need to move towards higher-resolution models, which in turn require high-resolution real-world observations that current instruments struggle to obtain. We present Cloud4D, the first learning-based framework that reconstructs a physically consistent, four-dimensional cloud state using only synchronized ground-based cameras. Leveraging a homography-guided 2D-to-3D transformer, Cloud4D infers the full 3D distribution of liquid water content at 25 m spatial and 5 s temporal resolution. By tracking the 3D liquid water content retrievals over time, Cloud4D additionally estimates horizontal wind vectors. Across a two-month deployment comprising six skyward cameras, our system delivers an order-of-magnitude improvement in space-time resolution relative to state-of-the-art satellite measurements, while retaining single-digit relative error ($<10\%$) against collocated radar measurements. Code and data are available on our project page https://cloud4d.jacob-lin.com/.
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