Radiative transfer and spectroscopic databases: A line-sampling Monte Carlo approach

Galtier, Mathieu; Blanco, Stéphane; Dauchet, Jérémi; Hafi, Mouna El; Eymet, Vincent; Fournier, Richard; Roger, Maxime; Spiesser, Christophe; Terrée, Guillaume

doi:10.1016/j.jqsrt.2015.10.016

Cited by 21 publications

(27 citation statements)

References 36 publications

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“…3 fork 1 = ka,max + ks,max. We check here that the alternative approach recovers the results of Table 1 in [1] with ζ = 1 (the extinction criterion defined in [5]). The computation times displayed in the last column correspond to an Intel Core i5 -2.5Ghz without parallelization.…”

Section: Appendix C the Additional Samplingmentioning

confidence: 61%

“…Then the choice is made to select a true-collision or a virtual-collision as function of their local respective-amounts and this is how the spatial information is recovered. But several other benefits were recently foreseen in [1] and practically tested in [5,6,7,8,9,10,11,12,13,14,15,16], mainly for radiative-transfer applications. The main idea is that null-collision algorithms transform the non-linearity of Beer-extinction into a linear-recursive problem that Monte Carlo handles without approximation [14].…”

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confidence: 99%

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Convergence issues in derivatives of Monte Carlo null-collision integral formulations: A solution

Tregan

Blanco

Dauchet

et al. 2020

Journal of Computational Physics

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When a Monte Carlo algorithm is used to evaluate a physical observable A, it is possible to slightly modify the algorithm so that it evaluates simultaneously A and the derivatives ∂ ς A of A with respect to each problem-parameter ς. The principle is the following: Monte Carlo considers A as the expectation of a random variable, this expectation is an integral, this integral can be derivated as function of the problem-parameter to give a new integral, and this new integral can in turn be evaluated using Monte Carlo. The two Monte Carlo computations (of A and ∂ ς A) are simultaneous when they make use of the same random samples, i.e. when the two integrals have the exact same structure. It was proven theoretically that this was always possible, but nothing insures that the two estimators have the same convergence properties: even when a large enough sample-size is used so that A is evaluated very accurately, the evaluation of ∂ ς A using the same sample can remain inaccurate. We discuss here such a pathological example:null-collision algorithms are very successful when dealing with radiative transfer in heterogeneous media, but they are sources of convergence difficulties as soon as sensitivity-evaluations are considered. We analyse theoretically these convergence difficulties and propose an alternative solution.first practical benefit is that the next collision event can be sampled as if the medium was homogenous. Then the choice is made to select a true-collision or a virtual-collision as function of their local respective-amounts and this is how the spatial information is recovered. But several other benefits were recently foreseen in [1] and practically tested in [5,6,7,8,9,10,11,12,13,14,15,16], mainly for radiative-transfer applications. The main idea is that null-collision algorithms transform the non-linearity of Beer-extinction into a linear-recursive problem that Monte Carlo handles without approximation [14]. This was for instance used in [5] to deal with absorption-spectra of molecular gases combining very numerous transitions: the summation over all transitions could be treated by the Monte Carlo algorithm itself, which was previously assumed impossible because this summation was inside the exponential of Beer-extinction. Similarly, the vanishing of the exponential allowed the extension of implicit Monte Carlo algorithms for inversion of absorption and scattering coefficients from intensity measurements [6]. Outside radiative transfer, a very similar idea was used to solve Electromagnetic Maxwell equations for energy propagation in particle-ensembles of statistically-distributed shapes despite of the nonlinearity associated to the square of the electric field [13]. Again similar is the algorithm proposed in [14] solving Boltzmann equation for micro-fluidics applications despite of the nonlinearity of the collision operator.Back to radiative-transfer applications, the ideas suggested in [1] have motivated significant developments in the computer-graphics community for the cinema industry. Here the benef...

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Section: Appendix C the Additional Samplingmentioning

confidence: 61%

mentioning

confidence: 99%

Convergence issues in derivatives of Monte Carlo null-collision integral formulations: A solution

Tregan

Blanco

Dauchet

et al. 2020

Journal of Computational Physics

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“…Differentiation to evaluate sensitivities is only one example of transformation based on the manipulation of integral formulations. Other examples include the handling of negative null‐collision coefficients (Galtier et al, ) and the sampling of absorption lines when the gaseous part of k cannot be precomputed in line‐by‐line MC algorithms dealing with large spectroscopic databases (Galtier et al, ). As soon as the introduction of null collisions is perceived as a formal way to handle the nonlinearity of Beer's extinction in heterogeneous fields, interpretation of the modified NCAs may depart widely from the intuitive adding of virtual scatterers.…”

Section: A Path‐tracing Librarymentioning

confidence: 99%

“…This explicit framework opened doors to new families of MC algorithms, with potential for solving various problems that were before then considered impossible: nonlinear models (Dauchet et al, 2018), coupled radiation-convection-conduction in a single MC algorithm , energetic state transitions sampled from spectroscopy instead of approximate spectral models (Galtier et al, 2016), symbolic MC in scattering media (Galtier et al, 2017), etc. Some of these methods are transposable to atmospheric radiative transfer with large benefits for our community, for example, conductoradiative MC models to investigate atmosphere-cities interactions, or line-sampling methods for benchmark spectral integration, to develop, tune and test spectral models.…”

Section: A2 Path Tracing and Complex Volumesmentioning

confidence: 99%

A Path‐Tracing Monte Carlo Library for 3‐D Radiative Transfer in Highly Resolved Cloudy Atmospheres

Villefranque

Fournier

Couvreux

et al. 2019

J Adv Model Earth Syst

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Interactions between clouds and radiation are at the root of many difficulties in numerically predicting future weather and climate and in retrieving the state of the atmosphere from remote sensing observations. The broad range of issues related to these interactions, and to three‐dimensional interactions in particular, has motivated the development of accurate radiative tools able to compute all types of radiative metrics, from monochromatic, local, and directional observables to integrated energetic quantities. Building on this community effort, we present here an open‐source library for general use in Monte Carlo algorithms. This library is devoted to the acceleration of ray tracing in complex data, typically high‐resolution large‐domain grounds and clouds. The main algorithmic advances embedded in the library are related to the construction and traversal of hierarchical grids accelerating the tracing of paths through heterogeneous fields in null‐collision (maximum cross‐section) algorithms. We show that with these hierarchical grids, the computing time is only weakly sensitive to the refinement of the volumetric data. The library is tested with a rendering algorithm that produces synthetic images of cloud radiances. Other examples of implementation are provided to demonstrate potential uses of the library in the context of 3‐D radiation studies and parameterization development, evaluation, and tuning.

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“…4 have been obtained using the HITRAN spectroscopic database. All other parameters are given in Galtier et al 2015 [50].…”

Section: Simulated Configurationmentioning

confidence: 99%

Addressing nonlinearities in Monte Carlo

Dauchet

Bézian

Blanco

et al. 2018

Sci Rep

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Monte Carlo is famous for accepting model extensions and model refinements up to infinite dimension. However, this powerful incremental design is based on a premise which has severely limited its application so far: a state-variable can only be recursively defined as a function of underlying state-variables if this function is linear. Here we show that this premise can be alleviated by projecting nonlinearities onto a polynomial basis and increasing the configuration space dimension. Considering phytoplankton growth in light-limited environments, radiative transfer in planetary atmospheres, electromagnetic scattering by particles, and concentrated solar power plant production, we prove the real-world usability of this advance in four test cases which were previously regarded as impracticable using Monte Carlo approaches. We also illustrate an outstanding feature of our method when applied to acute problems with interacting particles: handling rare events is now straightforward. Overall, our extension preserves the features that made the method popular: addressing nonlinearities does not compromise on model refinement or system complexity, and convergence rates remain independent of dimension.

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Radiative transfer and spectroscopic databases: A line-sampling Monte Carlo approach

Cited by 21 publications

References 36 publications

Convergence issues in derivatives of Monte Carlo null-collision integral formulations: A solution

Convergence issues in derivatives of Monte Carlo null-collision integral formulations: A solution

A Path‐Tracing Monte Carlo Library for 3‐D Radiative Transfer in Highly Resolved Cloudy Atmospheres

Addressing nonlinearities in Monte Carlo

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