@inproceedings{Lin:2025:10.48550/arXiv.2511.19431,
author = {Lin, J and Gryspeerdt, E and Clark, R},
doi = {10.48550/arXiv.2511.19431},
title = {Cloud4D: Estimating cloud properties at a high spatial and temporal resolution},
url = {http://dx.doi.org/10.48550/arXiv.2511.19431},
year = {2025}
}

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TY  - CPAPER
AB  - There has been great progress in improving numerical weather prediction and climate models using machine learning. However, most global models act at akilometer-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 projectpage https://cloud4d.jacob-lin.com/.
AU  - Lin,J
AU  - Gryspeerdt,E
AU  - Clark,R
DO  - 10.48550/arXiv.2511.19431
PY  - 2025///
TI  - Cloud4D: Estimating cloud properties at a high spatial and temporal resolution
UR  - http://dx.doi.org/10.48550/arXiv.2511.19431
ER  - 

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