HybridCT: ML Accelerated Tomography for Climate Relevant 3D Cloud Observations
arXiv:2611.16255
Linda Forster, Nicholas J. LaHaye, Marcin J. Kurowski, Anna Jungbluth, Bernhard Mayer, Anthony B. Davis
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Low clouds are a large hole in climate forecasts partly because their insides are hard to measure in three dimensions. HybridCT turns nine viewing angles into optical-thickness projections with a learned emulator, then inverts those projections into extinction in a few seconds.
Read free explainer →Low-level clouds are one of the largest sources of uncertainty in climate predictions, partly because we lack detailed three-dimensional (3D) measurements of their internal structure. Here, we present HybridCT, a cloud tomographic reconstruction scheme accelerated by machine learning (ML) that recovers 3D cloud extinction directly from passive multi-angle observations. An ML emulator maps radiances from multiple viewing angles to cloud optical thickness (COT), producing path-integrated projections, and a Simultaneous Algebraic Reconstruction Technique (SART) explicitly inverts these projections to retrieve 3D extinction. Training observations are generated from synthetic 3D cloud fields using radiative transfer simulations for nine viewing angles between $\pm$70$^{\circ}$. HybridCT reconstructs extinction with RMSE 13.3 km$^{-1}$ in 3.5 s, compared to 22.7 km$^{-1}$ in approx. 30 min for the state-of-the-art physics-based tomography framework AT3D, a speedup exceeding 500$\times$. HybridCT thus provides a scalable route toward large-scale observations of 3D cloud structure relevant to radiative-budget interpretation and evaluation of high-resolution cloud simulations.