Published August 30, 2025
| Version v1
Dataset
Open
HybridCT Dataset -- ground truth, training, predicted data, and model parameters
Authors/Creators
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1.
Jet Propulsion Laboratory
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2.
Ludwig-Maximilians-Universität München
Contributors
Project leader:
Project members:
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1.
Jet Propulsion Laboratory
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2.
National Aeronautics and Space Administration, Jet Propulsion Laboratory
Description
This dataset consists of three parts:
- Ground truth data: Microphysical cloud properties from Large Eddy Simulation for LWC (liquid water content) and reff (effective droplet radius) in 80x80x4 km domain with 40 m resolution.
- Training data: collection of input data and labels for training the HybridCT `emulator`
- Input: Synthetic multi-angle radiance fields which are generated from the ground truth cloud fields via radiative transfer simulations. The synthetic multi-angle images represent the viewing angle geometry of the Multi-angle Imaging Spectroradiometer (MISR) onboard the Terra spacecraft at its red channel (670 nm).
- Labels: Multi-angle optical thickness fields directly computed from the ground truth cloud fields.
- Predicted data: Multi-angle optical thickness fields predicted by the HybridCT `emulator` which serve as input for the `reconstruction`.
- Model artifacts: parameters for the trained U-Net machine learning model to predict optical thickness maps from multi-angle radiance input.
Files
artifacts_v2025.11.29.zip
Files
(47.0 GB)
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2.1 GB | Preview Download |
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172.4 MB | Download |
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md5:2db5c080ae6c28abb98ce1977688e990
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6.1 GB | Download |
Additional details
Funding
- National Aeronautics and Space Administration
- 80NM0018D0004