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3D Remote Sensing of Clouds  

Motivation and Context

Clouds influence both local weather and global climate, affecting water cycles, solar-power generation, and impacting Earth’s energy balance. Nevertheless, clouds and aerosols are the largest sources of uncertainty in global climate predictions (Intergovernmental Panel on Climate Change (IPCC) report). In light of that, the decadal survey for Earth Science has identified: understanding clouds and aerosols and their impacts on climate and weather as a top priority, with broad scientific and societal implications. Remote sensing largely assumes the atmosphere is made of horizontally uniform layers. This 1D paradigm fails to account for the complex 3D nature of clouds and 3D radiation transfer within (Davis & Marshak). This severely limits the ability to study cloud processes that are spatially variable from observations, shifting research towards 3D computer simulations like Large Eddy Simulations (LES). Cloud tomography offers a radically different approach that could reveal the 3D internal structure of real clouds (not simulated) and offer invaluable insights into cloud processes.

Computational Concept

From a radiative transfer standpoint, clouds are 3D scattering media. When the Sun illuminates a cloud, light is scattered multiple times within the cloud before reaching the sensor. As sunlight interacts with cloud droplets they leave behind subtle clues about their properties (density, sizes, shapes, etc). We developed a methodology to extract 3D cloud properties from scattered sunlight: Cloud Tomography. The key breakthrough was a tractable computational approach for estimating cloud properties while accounting for 3D radiative transfer(including multiple scattering). Below is a (very) short highlight clip and a more technical talk highlighting the breakthrough computational concept.

Short highlight clip on cloud tomography (ICCV 2015)

Technical talk detailing the formulation and algorithm (ICCV 2015)

This work has paved the way for a novel space mission: CloudCT.

is a space mission, directly derived from my Ph.D. research, to launch 10 pico-satellites that will orbit in a formation and gather multi-view observations of clouds. CloudCT, led by Yoav SchechnerIlan Koren, and Klaus Schilling, has won ERC Synergy funding of €14 million.​ For more information see the ClouCT page. The short clip below highlights the the CloudCT mission concept

The clips below are slightly more recent talks on our work to go beyond cloud densities and recover the 3D droplet sizes (microphysics) from information hidden in multiple wavelengths and polarization measurements

This talk was given in APOLO-2022

This talk was given in SPIE 2021



As part of an effort to make this research open, useful, and reproducible, we have spent many hours coding, re-coding, and documenting. The core radiative transfer routines are sourced from the Fortran SHDOM (Spherical Harmonic Discrete Ordinate Method) code by Frank K. Evans. I am particularly grateful for the guidance of Amit Aides in the initial stages of this project and the wonderful rigorous work of Jesse Loveridge.



  1. Jesse LoveridgeAviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony Davis, and Yoav Schechner, "Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: local optimization", Atmos. Meas. Tech., 2023.

  2. Jesse LoveridgeAviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony Davis, and Yoav Schechner, "Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation", Atmos. Meas. Tech., 2022.

  3. Aviad Levis, Anthony Davis, Jesse Loveridge, and Yoav Schechner "3D cloud tomography and droplet size retrieval from multi-angle polarimetric imaging of scattered sunlight from above", Proc. SPIE, Polarization Science and Remote Sensing X, 2021.

  4. Aviad Levis, Yoav Schechner, Anthony Davis, Jesse Loveridge, "Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size", Remote Sens. 2020.

  5. Tamar Loeub, Aviad Levis, Vadim Holodovsky, and Yoav Schechner, "Monotonicity Prior for Cloud Tomography" ECCV, 2020.

  6. Amit Aides, Aviad Levis, Vadim Holodovsky, Yoav Schechner, Dietrich Althausen, and Adi Vainiger, "Distributed Sky Imaging Radiometry and Tomography", Proc. IEEE ICCP, 2020.

  7. Felipe Mejia, Ben Kurtz, Aviad Levis, Íñigo de la Parra, Jan Kleissl, "Cloud tomography applied to sky images: A virtual testbed", Solar Energy, 2018.

  8. Aviad Levis, Yoav Schechner, Anthony DavisMultiple-Scattering Microphysics Tomography, Proc. IEEE CVPR, 2017.

  9. Vadim Holodovsky, Yoav Schechner, Anat Levin, Aviad Levis, Amit Aides, “In-Situ Multi-View Multi-Scattering Stochastic Tomography, Proc. IEEE ICCP, 2016.

  10. Aviad Levis, Yoav Schechner, Amit Aides, Anthony Davis, Airborne three-dimensional cloud tomography, Proc. IEEE ICCV, 2015.

  11. Danny Veikherman, Amit Aides, Yoav Schechner, and Aviad Levis,Clouds in The Cloud”, Proc. ACCV, 2014.

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