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/.
@article{arxiv.2511.19431,
title = {Cloud4D: Estimating Cloud Properties at a High Spatial and Temporal Resolution},
author = {Jacob Lin and Edward Gryspeerdt and Ronald Clark},
journal= {arXiv preprint arXiv:2511.19431},
year = {2025}
}