Analytical Jacobians of single scattering optical properties using the invariant imbedding T-matrix method

Sun, Bingqiang; Gao, Chenxu; Bi, Lei; Spurr, Robert

doi:10.1364/oe.421886

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…Such parameters in this study include aerosol properties such as AOD, SSA, aerosol effective radius, and R rs . Derivatives of aerosol properties can be either computed from an analytical function (e.g., effective size) or based on single scattering calculations (e.g., AOD, SSA), such as using Lorenz-Mie theory (Grainger et al, 2004;Spurr et al, 2012) or the T-Matrix method (Xu and Davis, 2011;Spurr et al, 2012;Sun et al, 2021). However, uncertainties for R rs are more challenging to quantify as they require additional radiative transfer simulations to conduct atmospheric and bidirectional reflectance distribution function (BRDF) corrections.…”

Section: Discussionmentioning

confidence: 99%

Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean

et al. 2022

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Abstract. Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere–ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and the nonlinear dependence of the forward model on the retrieved parameters near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on efficient neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the uncertainty evaluation technique in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, as well as guide MAP algorithm development for current and future satellite systems such as NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

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Section: Discussionmentioning

confidence: 99%

Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean

et al. 2022

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“…effective size) or based on single scattering calculations (e.g. AOD, SSA), such as using Lorenz-Mie theory (Grainger et al, 2004;Spurr et al, 2012) or the T-Matrix method (Xu and Davis, 2011;Spurr et al, 2012;Sun et al, 2021). Uncertainties for R rs are more challenging to quantify as they require additional radiative transfer simulations to conduct atmospheric and bidirectional reflectance distribution function (BRDF) corrections.…”

Section: Speed Improvement Using Automatic Differentiationmentioning

confidence: 99%

Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean, Part 1: performance evaluation and speed improvement

Gao

Knobelspiesse

Franz

et al. 2022

Preprint

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Abstract. Multi-angle polarimetric (MAP) measurements can enable detailed characterization of aerosol microphysical and optical properties and improve atmospheric correction in ocean color remote sensing. Advanced retrieval algorithms have been developed to obtain multiple geophysical parameters in the atmosphere-ocean system. Theoretical pixel-wise retrieval uncertainties based on error propagation have been used to quantify retrieval performance and determine the quality of data products. However, standard error propagation techniques in high-dimensional retrievals may not always represent true retrieval errors well due to issues such as local minima and nonlinearity of radiative transfer near the solution. In this work, we analyze these theoretical uncertainty estimates and validate them using a flexible Monte Carlo approach. The Fast Multi-Angular Polarimetric Ocean coLor (FastMAPOL) retrieval algorithm, based on several neural network forward models, is used to conduct the retrievals and uncertainty quantification on both synthetic HARP2 (Hyper-Angular Rainbow Polarimeter 2) and AirHARP (airborne version of HARP2) datasets. In addition, for practical application of the technique to uncertainty evaluation in operational data processing, we use the automatic differentiation method to calculate derivatives analytically based on the neural network models. Both the speed and accuracy associated with uncertainty quantification for MAP retrievals are addressed in this study. Pixel-wise retrieval uncertainties are further evaluated for the real AirHARP field campaign data. The uncertainty quantification methods and results can be used to evaluate the quality of data products, and guide MAP algorithm development for current and future satellite systems such as NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission.

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“…The direct calculation of the linearized radiative transfer model generates the required Jacobians, as has been applied practically [47][48][49][50]. Meanwhile, the derivatives of single scattering properties with respect to optical parameters, such as refractive index, and shape parameters, such as aspect ratio and effective radius, can be generated from the linearization of scattering algorithms [51]. The aforementioned algorithms, such as IITM, EBCM, and PGOM, have been linearized and validated by the finite difference method and against each other [51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, the derivatives of single scattering properties with respect to optical parameters, such as refractive index, and shape parameters, such as aspect ratio and effective radius, can be generated from the linearization of scattering algorithms [51]. The aforementioned algorithms, such as IITM, EBCM, and PGOM, have been linearized and validated by the finite difference method and against each other [51][52][53][54]. Previous studies have shown that single scattering properties calculated by the linearized IITM and PGOM (abbreviated as LIITM and LPGOM) are sufficiently accurate for a wide range of size parameters [55].…”

Section: Introductionmentioning

confidence: 99%

Linearized Single-Scattering Property Database for Hexagonal Prism Ice Particles

et al. 2022

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Accurate description of the single scattering properties of atmospheric particles can be an essential factor influencing the remote sensing of atmospheric microphysics. In this paper, a database for the linearized single scattering properties of ice particles was developed in the visible to infrared spectral region of 0.4–15 μm and for size parameters ranging from 0.5 to 500. The linearized invariant imbedding T-matrix method and linearized physical-geometric optics method were jointly applied. A full set of integral scattering properties including extinction efficiency, single scattering albedo, asymmetry factors, and differential scattering properties, including six phase matrix elements, were the basic scattering parameters in the database. Furthermore, the Jacobians of these regular scattering properties with respect to refractive index (real and imaginary parts) and effective radius were also included and used for sensitivity determinations. The spectral and size-dependent variations and changing rates of the derivative characteristics with actual application values, such as backscattering depolarization ratios, were also discussed.

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Analytical Jacobians of single scattering optical properties using the invariant imbedding T-matrix method

Cited by 10 publications

References 27 publications

Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean

Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean

Effective uncertainty quantification for multi-angle polarimetric aerosol remote sensing over ocean, Part 1: performance evaluation and speed improvement

Linearized Single-Scattering Property Database for Hexagonal Prism Ice Particles

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