Monte Carlo Simulation of Solar Reflectances for Cloudy Atmospheres

Barker, Howard; Goldstein, Rhys; Stevens, Dave

doi:10.1175/1520-0469(2003)060<1881:mcsosr>2.0.co;2

Cited by 50 publications

(40 citation statements)

References 14 publications

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“…Time savings result because the sample terminates when w9 5 0. Unlike simply truncating very weak samples (see, e.g., section 5a of Barker et al 2003), Russian roulette does not introduce bias, but it decreases the time to solution by an amount depending on the optical depth of the medium, the single scattering albedo, and the surface albedo. Figure 2c shows the ratio of the cost of radiation transport (e.g., flux calculations) computed using Eq.…”

Section: Methods For Reducing Computation Timementioning

confidence: 99%

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Computational Cost and Accuracy in Calculating Three-Dimensional Radiative Transfer: Results for New Implementations of Monte Carlo and SHDOM

Pincus

Evans

2009

Journal of the Atmospheric Sciences

106

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This paper examines the tradeoffs between computational cost and accuracy for two new state-of-the-art codes for computing three-dimensional radiative transfer: a community Monte Carlo model and a parallel implementation of the Spherical Harmonics Discrete Ordinate Method (SHDOM). Both codes are described and algorithmic choices are elaborated. Two prototype problems are considered: a domain filled with stratocumulus clouds and another containing scattered shallow cumulus, absorbing aerosols, and molecular scatterers. Calculations are performed for a range of resolutions and the relationships between accuracy and computational cost, measured by memory use and time to solution, are compared.Monte Carlo accuracy depends primarily on the number of trajectories used in the integration. Monte Carlo estimates of intensity are computationally expensive and may be subject to large sampling noise from highly peaked phase functions. This noise can be decreased using a range of variance reduction techniques, but these techniques can compromise the excellent agreement between the true error and estimates obtained from unbiased calculations. SHDOM accuracy is controlled by both spatial and angular resolution; different output fields are sensitive to different aspects of this resolution, so the optimum accuracy parameters depend on which quantities are desired as well as on the characteristics of the problem being solved. The accuracy of SHDOM must be assessed through convergence tests and all results from unconverged solutions may be biased.SHDOM is more efficient (i.e., has lower error for a given computational cost) than Monte Carlo when computing pixel-by-pixel upwelling fluxes in the cumulus scene, whereas Monte Carlo is more efficient in computing flux divergence and downwelling flux in the stratocumulus scene, especially at higher accuracies. The two models are comparable for downwelling flux and flux divergence in cumulus and upwelling flux in stratocumulus. SHDOM is substantially more efficient when computing pixel-by-pixel intensity in multiple directions; the models are comparable when computing domain-average intensities. In some cases memory use, rather than computation time, may limit the resolution of SHDOM calculations.

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Section: Methods For Reducing Computation Timementioning

confidence: 99%

“…One alternative is to modify the calculation, typically by modifying the scattering phase function used for local estimation (see, e.g., Antyufeev 1996; Barker et al 2003;Iwabuchi 2006). This reduces the variance between batches but may introduce biases in intensity estimates.…”

Section: Calculationsmentioning

confidence: 99%

Computational Cost and Accuracy in Calculating Three-Dimensional Radiative Transfer: Results for New Implementations of Monte Carlo and SHDOM

Pincus

Evans

2009

Journal of the Atmospheric Sciences

106

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“…Absorption is taken into account by multiplying the photon weight by the local singlescattering albedo. For reasons of variance reduction and computing time, techniques as proposed by Barker et al (2003) have been implemented. For calculating the radiance field, the local estimate method is more efficient than the common Monte Carlo photon counting method because no photons get lost.…”

Section: Technical Detailsmentioning

confidence: 99%

Disk and circumsolar radiances in the presence of ice clouds

et al. 2017

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Abstract. The impact of ice clouds on solar disk and circumsolar radiances is investigated using a Monte Carlo radiative transfer model. The monochromatic direct and diffuse radiances are simulated at angles of 0 to 8 • from the center of the sun. Input data for the model are derived from measurements conducted during the 2010 Small Particles in Cirrus (SPAR-TICUS) campaign together with state-of-the-art databases of optical properties of ice crystals and aerosols. For selected cases, the simulated radiances are compared with groundbased radiance measurements obtained by the Sun and Aureole Measurements (SAM) instrument.First, the sensitivity of the radiances to the ice cloud properties and aerosol optical thickness is addressed. The angular dependence of the disk and circumsolar radiances is found to be most sensitive to assumptions about ice crystal roughness (or, more generally, non-ideal features of ice crystals) and size distribution, with ice crystal habit playing a somewhat smaller role. Second, in comparisons with SAM data, the ice cloud optical thickness is adjusted for each case so that the simulated radiances agree closely (i.e., within 3 %) with the measured disk radiances. Circumsolar radiances at angles larger than ≈ 3 • are systematically underestimated when assuming smooth ice crystals, whereas the agreement with the measurements is better when rough ice crystals are assumed. Our results suggest that it may well be possible to infer the particle roughness directly from ground-based SAM measurements. In addition, the results show the necessity of correcting the ground-based measurements of direct radiation for the presence of diffuse radiation in the instrument's field of view, in particular in the presence of ice clouds.

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“…Barker et al, 2003;Cahalan et al, 2005). Their advantage over the use of statistical model data (e.g.…”

Section: Cloud Resolving Simulation Of Deep Convectionmentioning

confidence: 99%

Remote sensing of cloud sides of deep convection: towards a three-dimensional retrieval of cloud particle size profiles

et al. 2008

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Abstract. The cloud scanner sensor is a central part of a recently proposed satellite remote sensing concept -the three-dimensional (3-D) cloud and aerosol interaction mission (CLAIM-3D) combining measurements of aerosol characteristics in the vicinity of clouds and profiles of cloud microphysical characteristics. Such a set of collocated measurements will allow new insights in the complex field of cloudaerosol interactions affecting directly the development of clouds and precipitation, especially in convection. The cloud scanner measures radiance reflected or emitted by cloud sides at several wavelengths to derive a profile of cloud particle size and thermodynamic phase. For the retrieval of effective size a Bayesian approach was adopted and introduced in a preceding paper.In this paper the potential of the approach, which has to account for the complex three-dimensional nature of cloud geometry and radiative transfer, is tested in realistic cloud observing situations. In a fully simulated environment realistic cloud resolving modelling provides complex 3-D structures of ice, water, and mixed phase clouds, from the early stage of convective development to mature deep convection. A three-dimensional Monte Carlo radiative transfer is used to realistically simulate the aspired observations.A large number of cloud data sets and related simulated observations provide the database for an experimental Bayesian retrieval. An independent simulation of an additional cloud field serves as a synthetic test bed for the demonCorrespondence to: T. Zinner (tobias.zinner@dlr.de) stration of the capabilities of the developed retrieval techniques. For this test case only a minimal overall bias in the order of 1% as well as pixel-based uncertainties in the order of 1 µm for droplets and 8 µm for ice particles were found for measurements at a high spatial resolution of 250 m.

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Monte Carlo Simulation of Solar Reflectances for Cloudy Atmospheres

Cited by 50 publications

References 14 publications

Computational Cost and Accuracy in Calculating Three-Dimensional Radiative Transfer: Results for New Implementations of Monte Carlo and SHDOM

Computational Cost and Accuracy in Calculating Three-Dimensional Radiative Transfer: Results for New Implementations of Monte Carlo and SHDOM

Disk and circumsolar radiances in the presence of ice clouds

Remote sensing of cloud sides of deep convection: towards a three-dimensional retrieval of cloud particle size profiles

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