Settings for Spaceborne 3-D Scattering Tomography of Liquid-Phase Clouds by the CloudCT Mission

Tzabari, Masada; Holodovsky, Vadim; Shubi, Omer; Eytan, Eshkol; Koren, Ilan; Schechner, Yoav Y.

doi:10.1109/tgrs.2022.3198525

Cited by 5 publications

(11 citation statements)

References 64 publications

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“…Correspondingly, developing more effective initialization strategies and other acceleration methods will improve the retrieval problem. Methods to estimate the order of magnitude of the optical properties within the cloud have been proposed based on either a grid search of a low-dimensional cloud model (Tzabari et al, 2022) or analytic radiative transfer results for idealized cloud geometries (Davis et al, 2021). Both of these methods require cloud envelope information as input and stereo methods have shown promise at providing the required information (Dandini et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…We perform several "inverse crimes" in our retrievals by choosing the discretization of the retrieved medium to perfectly match the ground truth and neglecting several important sources of uncertainty such as forward model error and instrument calibration uncertainties. The first of these approximations has been routine throughout numerical studies of cloud tomography (Levis et al, 2015(Levis et al, , 2017Martin and Hasekamp, 2018;Levis et al, 2020;Doicu et al, 2022a, b;Tzabari et al, 2022). Such approximations are also common in the assessment of other algorithms for atmospheric remote sensing, where synthetic measurement data are often generated using the same 1D radiative transfer model used to perform the retrievals (Delanoë and Hogan, 2008;Xu et al, 2022).…”

Section: Methodsmentioning

confidence: 99%

“…It is still unclear exactly how effective tomography will be across the range of scattering regimes present in the Earth's atmosphere. The development of tomographic retrievals in atmospheric science is at an early stage where numerical tests have yet to consider the full complexity of Earth's atmosphere and surface (Martin and Hasekamp, 2018;Levis et al, 2020;Doicu et al, 2022b;Tzabari et al, 2022). This two-part study contributes to further our understanding of the effectiveness of cloud tomography.…”

Section: Introductionmentioning

confidence: 95%

“…In Part 1 of this study (Loveridge et al, 2022b), we described a remote sensing retrieval technique with the potential to meet these needs by providing 3D instantaneous snapshots of volumetric properties of the atmosphere at the resolution of passive imagery. Our method uses multi-angle imagery and the Spherical Harmonics Discrete Ordinates Method (SHDOM) for modelling 3D Radiative Transfer (RT) to constrain the 3D properties of atmospheric particles, such as effective particle radius and mass concentration (Levis et al, 2020;Tzabari et al, 2022), in a process called tomography (Arridge and Schotland, 2009;Martin et al, 2014). Our retrieval algorithm for the tomography problem directly builds upon earlier work in making such retrievals tangible and computationally efficient through the use of approximate Jacobians of 3D radiative transfer that enable the use of efficient gradient-based local optimization methods (Levis et al, 2015(Levis et al, , 2017(Levis et al, , 2020.…”

Section: Introductionmentioning

confidence: 99%

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Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: local optimization

Loveridge

Levis

Girolamo

et al. 2023

Preprint

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Abstract. Our global understanding of clouds and aerosols relies on the remote sensing of their optical, microphysical, and macrophysical properties using, in part, scattered solar radiation. Current retrievals assume clouds and aerosols form plane-parallel, homogeneous layers and utilize 1D radiative transfer (RT) models. These assumptions limit the detail that can be retrieved about the 3D variability of cloud and aerosol fields and induce biases in the retrieved properties for highly heterogeneous structures such as cumulus clouds and smoke plumes. In Part 1 of this two-part study, we validated a tomographic method that utilizes multi-angle passive imagery to retrieve 3D distributions of species using 3D RT to overcome these issues. That validation characterized the uncertainty in the approximate Jacobian used in the tomographic retrieval over a wide range of atmospheric and surface conditions for several horizontal boundary conditions. Here in Part 2, we test the algorithm’s effectiveness on synthetic data to test whether retrieval accuracy is limited by the use of the approximate Jacobian. We retrieve 3D distributions of volume extinction coefficient (σ3D) at 40 m resolution from synthetic multi-angle, mono-spectral imagery at 35 m resolution derived from stochastically-generated ‘cumuliform’ clouds in (1 km)3 domains. The retrievals are idealized in that we neglect forward modelling and instrumental errors with the exception of radiometric noise; thus reported retrieval errors are lower bounds. σ3D is retrieved with, on average, a Relative Root Mean Square Error (RRMSE) < 20 % and bias < 0.1 % for clouds with Maximum Optical Depth (MOD) < 17, and the RRMSE of the radiances is < 0.5 %, indicating very high accuracy in shallow cumulus conditions. As the MOD of the clouds increases to 80, the RRMSE and biases in σ3D worsen to 60 % and −35 %, respectively, and the RRMSE of the radiances reaches 16 %, indicating incomplete convergence. This is expected from the increasing ill-conditioning of the inverse problem with decreasing mean-free-path predicted by RT theory and discussed in detail in Part 1. We tested retrievals that use a forward model that is better conditioned but less accurate due to more aggressive delta-M scaling. This reduces the radiance RRMSE to 9 % and the bias in σ3D to −8 % in clouds with MOD ~80, with no improvement in the RRMSE of σ3D. This illustrates a significant sensitivity of the retrieval to the numerical configuration of the RT model which, at least in our circumstances, improves the retrieval accuracy. All of these ensemble-averaged results are robust to the inclusion of radiometric noise during the retrieval. However, individual realizations can have large deviations of up to 18 % in the mean extinction in clouds with MOD ~80, which indicates large uncertainties in the retrievals in the optically thick limit. Using the better conditioned forward model tomography can also accurately infer optical depths (OD) in conditions spanning the majority of oceanic, cumulus fields (MOD < 80) as the retrieval provides OD with bias and RRMSE better than −8 % and 36 %, respectively. This is a significant improvement over retrievals using 1D RT, which have OD biases between −30 % and −23 % and RRMSE between 29 % and 80 % for the clouds used here. Prior information or other sources of information will be required to improve the RRMSE of σ3D in the optically thick limit, where the RRMSE is shown to have strong spatial structure that varies with the solar and viewing geometry.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: local optimization

Loveridge

Levis

Girolamo

et al. 2023

Preprint

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“…Scattering-based CT uses two dimensional (2D) images (view projections) of the scene from multiple directions, using incoherent radiation. However, most techniques proposed so far use a traditional physicsbased optimization [30], [31], [32], [33], [34], which is slow and unscalable.…”

Section: Introductionmentioning

confidence: 99%

Variable Imaging Projection Cloud Scattering Tomography

Ronen¹,

Holodovsky²,

Schechner³

2024

IEEE Trans. Pattern Anal. Mach. Intell.

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Scattering-based computed tomography (CT) recovers a heterogeneous volumetric scattering medium using images taken from multiple directions. It is a nonlinear problem. Prior art mainly approached it by explicit physics-based optimization of image-fitting, being slow and difficult to scale. Scale is particularly important when the objects constitute large cloud fields, where volumetric recovery is important for climate studies. Besides speed, imaging and recovery need to be flexible, to efficiently handle variable viewing geometries and resolutions. These can be caused by perturbation in camera poses or fusion of data from different types of observational sensors. There is a need for fast variable imaging projection scattering tomography of clouds (VIP-CT). We develop a learning-based solution, using a deep-neural network (DNN) which trains on a large physics-based labeled volumetric dataset. The DNN parameters are oblivious to the domain scale, hence the DNN can work with arbitrarily large domains. VIP-CT offers much better quality than the state of the art. The inference speed and flexibility of VIP-CT make it effectively real-time in the context of spaceborne observations. The paper is the first to demonstrate CT of a real cloud using empirical data directly in a DNN. VIP-CT may offer a model for a learning-based solution to nonlinear CT problems in other scientific domains. Our code is available online.

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Unifying radiative transfer models in computer graphics and remote sensing, Part I: A survey

Salesin,

Knobelspiesse,

Chowdhary

et al. 2024

Journal of Quantitative Spectroscopy and Radiative Transfer

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Settings for Spaceborne 3-D Scattering Tomography of Liquid-Phase Clouds by the CloudCT Mission

Cited by 5 publications

References 64 publications

Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: local optimization

Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: local optimization

Variable Imaging Projection Cloud Scattering Tomography

Unifying radiative transfer models in computer graphics and remote sensing, Part I: A survey

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