Modeling polarized radiative transfer in the ocean-atmosphere system with the GPU-accelerated SMART-G Monte Carlo code

Ramon, Didier; Steinmetz, François; Jolivet, Dominique; Compiègne, M.; Frouin, Robert

doi:10.1016/j.jqsrt.2018.10.017

Cited by 43 publications

(23 citation statements)

References 37 publications

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“…For these computations, the number of underwater light quadrature points used for AOS-I and AOS-III models was set at 80 to adequately capture refraction of light by the ocean surface, while in AOS-IV model it was set at 300 to capture the sharp unnormalized diffraction peaks (corresponding asymmetry parameters ≥ 0.95) for scattering by D-P particulates. A fourth code, i.e., the Monte Carlo RT code SMART-G described in Ramon et al (2019), was also used for OAS-I model computations in which the rough ocean surface was treated as a BRDF surface. The number of photons used for the latter computations was set at 3 × 10 11 .…”

Section: Pace Updates (Fwd Rt Studies): Tabulated Benchmark Resultsmentioning

confidence: 99%

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Modeling Atmosphere-Ocean Radiative Transfer: A PACE Mission Perspective

et al. 2019

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The research frontiers of radiative transfer (RT) in coupled atmosphere-ocean systems are explored to enable new science and specifically to support the upcoming Plankton, Aerosol, Cloud ocean Ecosystem (PACE) satellite mission. Given (i) the multitude of atmospheric and oceanic constituents at any given moment that each exhibits a large variety of physical and chemical properties and (ii) the diversity of light-matter interactions (scattering, absorption, and emission), tackling all outstanding RT aspects related to interpreting and/or simulating light reflected by atmosphere-ocean systems becomes impossible. Instead, we focus on both theoretical and experimental studies of RT topics important to the science threshold and goal questions of the PACE mission and the measurement capabilities of its instruments. We differentiate between (a) forward (FWD) RT studies that focus mainly on sensitivity to influencing variables and/or simulating data sets, and (b) inverse (INV) RT studies that also involve the retrieval of atmosphere and ocean parameters. Our topics cover (1) the ocean (i.e., water body): absorption and elastic/inelastic scattering by pure water (FWD RT) and models for scattering and absorption by particulates (FWD RT and INV RT); (2) the airwater interface: variations in ocean surface refractive index (INV RT) and in whitecap reflectance (INV RT); (3) the atmosphere: polarimetric and/or hyperspectral remote sensing of aerosols (INV RT) and of gases (FWD RT); and (4) atmosphere-ocean systems: benchmark comparisons, impact of the Earth's sphericity and adjacency effects on space-borne observations, and scattering in the ultraviolet regime (FWD RT). We provide for each topic a summary of past relevant (heritage) work, followed by a discussion (for unresolved questions) and RT updates.

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Section: Pace Updates (Fwd Rt Studies): Tabulated Benchmark Resultsmentioning

confidence: 99%

“…The SMART-G radiation transfer code (Ramon et al, 2019) was used to examine the effects of Earth's sphericity on the radiance emerging from the atmosphere. This code is based on the Monte Carlo technique, works in either plane-parallel or in spherical-shell geometry, and accounts for polarization.…”

Section: Pace Updates (Fwd Rt Studies): Spherical Shellsmentioning

confidence: 99%

Modeling Atmosphere-Ocean Radiative Transfer: A PACE Mission Perspective

et al. 2019

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“…The reflectance of land is 0.8 (typical value for snow) and isotropic, chlorophyll-a concentration is 1 mgm −3 , aerosols are of maritime type with scale height of 2 km and optical thickness of 0.3 at 550 nm, and wind speed is 5 m s −1 . The simulations are performed with a Monte Carlo Code operated in backward mode (Ramon et al, 2019). No assumptions are made regarding interactions between the surface and the atmosphere.…”

Section: Variabilitymentioning

confidence: 99%

“…This was also the conclusion of Ding and Gordon (1995), who suggested that for those angles a sufficient treatment would be to compute the molecular reflectance with a spherical-shell atmosphere RT code. It is important to emphasize, however, that the effect of Earth's curvature is not only to increase intensity at grazing Sun zenith angles due to the smaller attenuation of the direct solar beam, but also to lower intensity at low Sun zenith angles due to a smaller illumination volume (e.g., Chowdhary et al, 2019, this issue;Ramon et al, 2019). This second effect is generally understated, but critical for accurate atmospheric correction.…”

Section: Earth's Curvaturementioning

confidence: 99%

Atmospheric Correction of Satellite Ocean-Color Imagery During the PACE Era

et al. 2019

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The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will carry into space the Ocean Color Instrument (OCI), a spectrometer measuring at 5 nm spectral resolution in the ultraviolet (UV) to near infrared (NIR) with additional spectral bands in the shortwave infrared (SWIR), and two multi-angle polarimeters that will overlap the OCI spectral range and spatial coverage, i. e., the Spectrometer for Planetary Exploration (SPEXone) and the Hyper-Angular Rainbow Polarimeter (HARP2). These instruments, especially when used in synergy, have great potential for improving estimates of water reflectance in the post Earth Observing System (EOS) era. Extending the topof-atmosphere (TOA) observations to the UV, where aerosol absorption is effective, adding spectral bands in the SWIR, where even the most turbid waters are black and sensitivity to the aerosol coarse mode is higher than at shorter wavelengths, and measuring in the oxygen A-band to estimate aerosol altitude will enable greater accuracy in atmospheric correction for ocean color science. The multi-angular and polarized measurements, sensitive to aerosol properties (e.g., size distribution, index of refraction), can further help to identify or constrain the aerosol model, or to retrieve directly water reflectance. Algorithms that exploit the new capabilities are presented, and their ability to improve accuracy is discussed. They embrace a modern, adapted heritage two-step algorithm and alternative schemes (deterministic, statistical) that aim at inverting the TOA signal in a single step. These schemes, by the nature of their construction, their robustness, their generalization properties, and their ability Frouin et al. Atmospheric Correction of Ocean-Color Imagery to associate uncertainties, are expected to become the new standard in the future. A strategy for atmospheric correction is presented that ensures continuity and consistency with past and present ocean-color missions while enabling full exploitation of the new dimensions and possibilities. Despite the major improvements anticipated with the PACE instruments, gaps/issues remain to be filled/tackled. They include dealing properly with whitecaps, taking into account Earth-curvature effects, correcting for adjacency effects, accounting for the coupling between scattering and absorption, modeling accurately water reflectance, and acquiring a sufficiently representative dataset of water reflectance in the UV to SWIR. Dedicated efforts, experimental and theoretical, are in order to gather the necessary information and rectify inadequacies. Ideas and solutions are put forward to address the unresolved issues. Thanks to its design and characteristics, the PACE mission will mark the beginning of a new era of unprecedented accuracy in ocean-color radiometry from space.

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“…Several approaches, including Monte Carlo simulation [25][26][27][28], the invariant imbedding method [23], and the discrete ordinate method [29], have been used to obtain numerical solutions for the RTE of water via the necessary boundary conditions. Several vector radiative transfer models of coupled ocean-atmosphere system (e.g., studies in References [30][31][32][33][34][35][36]) were developed to predict the radiance and degree of polarization of the light using various numerical methods.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Weighting Average Functions as a Simplification of the Radiative Transfer Simulation in Vertically Inhomogeneous Eutrophic Waters

Xue

2019

Applied Sciences

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Current water color remote sensing algorithms typically do not consider the vertical variations of phytoplankton. Ecolight with a radiative transfer program was used to simulate the underwater light field of vertical inhomogeneous waters based on the optical properties of a eutrophic lake (i.e., Lake Chaohu, China). Results showed that the vertical distribution of chlorophyll-a (Chla(z)) can considerably affect spectrum shape and magnitude of apparent optical properties (AOPs), including subsurface remote sensing reflectance in water (rrs(λ, z)) and the diffuse attenuation coefficient (Kx(λ, z)). The vertical variations of Chla(z) changed the spectrum shapes of rrs(λ, z) at the green and red wavelengths with a maximum value at approximately 590 nm, and changed the Kx(λ, z) from blue to red wavelength range with no obvious spectral variation. The difference between rrs(λ, z) at depth z m and its asymptotic value (Δrrs(λ, z)) could reach to ~78% in highly stratified waters. Diffuse attenuation coefficient of downwelling plane irradiance (Kd(λ, z)) had larger vertical variations, especially near water surface, in highly stratified waters. Three weighting average functions performed well in less stratified waters, and the weighting average function proposed by Zaneveld et al., (2005) performed best in highly stratified waters. The total contribution of the first three layers to rrs(λ, 0−) was approximately 90%, but the contribution of each layer in the water column to rrs(λ, 0−) varied with wavelength, vertical distribution of Chla(z) profiles, concentration of suspended particulate inorganic matter (SPIM), and colored dissolved organic matter (CDOM). A simple stratified remote sensing reflectance model considering the vertical distribution of phytoplankton was built based on the contribution of each layer to rrs(λ, 0−).

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Modeling polarized radiative transfer in the ocean-atmosphere system with the GPU-accelerated SMART-G Monte Carlo code

Cited by 43 publications

References 37 publications

Modeling Atmosphere-Ocean Radiative Transfer: A PACE Mission Perspective

Modeling Atmosphere-Ocean Radiative Transfer: A PACE Mission Perspective

Atmospheric Correction of Satellite Ocean-Color Imagery During the PACE Era

Evaluation of Weighting Average Functions as a Simplification of the Radiative Transfer Simulation in Vertically Inhomogeneous Eutrophic Waters

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