Monotonicity Prior for Cloud Tomography

Loeub, Tamar; Levis, Aviad; Holodovsky, Vadim; Schechner, Yoav Y.

doi:10.1007/978-3-030-58523-5_17

Cited by 15 publications

(53 citation statements)

References 48 publications

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“…The suggested cloud model assumes monotonic profiles of LWC and r e with altitude Z ≥ Z 0 within a cloud, Z 0 being the cloud-base height [27]. The LWC is assumed to have a linear profile:…”

Section: A Monotonous Modelmentioning

confidence: 99%

Advances in 3D scattering tomography of cloud micro-physics

Tzabari,

Holodovsky,

Shubi

et al. 2021

Preprint

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We introduce new adjustments and advances in space-borne 3D volumetric scattering-tomography of cloud micro-physics. The micro-physical properties retrieved are the liquid water content and effective radius within a cloud. New adjustments include an advanced perspective polarization imager model, and the assumption of 3D variation of the effective radius. Under these assumptions, we advanced the retrieval to yield results that (compared to the simulated ground-truth) have smaller errors than the prior art. Elements of our advancement include initialization by a parametric horizontally-uniform micro-physical model. The parameters of this initialization are determined by a grid search of the cost function. Furthermore, we added viewpoints corresponding to single-scattering angles, where polarization yields enhanced sensitivity to the droplet micro-physics (i.e., the cloudbow region). In addition, we introduce an optional adjustment, in which optimization of the liquid water content and effective radius are separated to alternating periods. The suggested initialization model and additional advances have been evaluated by retrieval of a set of large-eddy simulation clouds.

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Section: A Monotonous Modelmentioning

confidence: 99%

Advances in 3D scattering tomography of cloud micro-physics

Tzabari,

Holodovsky,

Shubi

et al. 2021

Preprint

Self Cite

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“…Define radiative transfer by an operator RT(β t ). There are various algorithms to implement RT(β t ), including Monte-Carlo [33,35] and the spherical harmonic discrete ordinate method (SHDOM). We use the latter, as it is considered trustworthy by the scientific community [12] and has open-source online codes [29].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…When more than two cameras operate, localization behaves more moderately, but with a similar trend. Consider two criteria that had been used in 3D CT [23,30,33]. Per time sample t,…”

Section: Tomographic Angular Extentmentioning

confidence: 99%

“…Computing the Jacobian ∂F (β t ) /∂β t is complex then. However, there are approximations to the Jacobian of 3D RT, which can be computed efficiently [30,33], making the recovery tractable. The complexity of solving Eq.…”

Section: D Scattering Tomographymentioning

confidence: 99%

“…Computing the Jacobian ∂F (β t ) /∂β t is complex, thus it is established numerically by a surrogate function that evolves through iterations [30,33]. Calculating the gradient includes two dominant time-consuming processes that are executed in alternation.…”

Section: Computational Complexitymentioning

confidence: 99%

See 2 more Smart Citations

Spatiotemporal tomography based on scattered multiangular signals and its application for resolving evolving clouds using moving platforms

Ronen,

Schechner,

Eytan

2020

Preprint

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We derive computed tomography (CT) of a time-varying volumetric translucent object, using a small number of moving cameras. We particularly focus on passive scattering tomography, which is a non-linear problem. We demonstrate the approach on dynamic clouds, as clouds have a major effect on Earth's climate. State of the art scattering CT assumes a static object. Existing 4D CT methods rely on a linear image formation model and often on significant priors. In this paper, the angular and temporal sampling rates needed for a proper recovery are discussed. If these rates are used, the paper leads to a representation of the time-varying object, which simplifies 4D CT tomography. The task is achieved using gradient-based optimization. We demonstrate this in physics-based simulations and in an experiment that had yielded real-world data.

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Tomography of Turbulence Strength Based on Scintillation Imaging

Shaul

Schechner

2022

Lecture Notes in Computer Science

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Monotonicity Prior for Cloud Tomography

Cited by 15 publications

References 48 publications

Advances in 3D scattering tomography of cloud micro-physics

Advances in 3D scattering tomography of cloud micro-physics

Spatiotemporal tomography based on scattered multiangular signals and its application for resolving evolving clouds using moving platforms

Tomography of Turbulence Strength Based on Scintillation Imaging

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