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

Abstract

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.

Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement

“…By developing fast and accurate radiative tools that account for the full 3D radiative transfer in LES cloud scene, as proposed by Villefranque et al. (2019), we can compute many types of radiative metrics, from monochromatic, local, and directional observable to integrated energetic quantities. The use of such radiative metrics will allow us to tackle calibration of radiative parameterizations but also to better link the calibration realized at the level of the parameterizations itself with the one realized for the final full 3D model calibration, which mainly targets the radiative forcing of the atmospheric general circulation.…”

“…An effort is currently done in that direction in parallel to the work presented here, consisting in providing reference radiative transfer computations on the classical cloud test cases currently tion of atmospheric transport and macrophysics of clouds, but the radiative transfer computations run in LES models were often not more reliable than those used in GCM, preventing the use of radiative metrics. By developing fast and accurate radiative tools that account for the full 3D radiative transfer in LES cloud scene, as proposed by Villefranque et al (2019), we can compute many types of radiative metrics, from monochromatic, local, and directional observable to integrated energetic quantities. The use of such radiative metrics will allow us to tackle calibration of radiative parameterizations but also to better link the calibration realized at the level of the parameterizations itself with the one realized for the final full 3D model calibration, which mainly targets the radiative forcing of the atmospheric general circulation.…”

The Actuator Line Method in the Meteorological LES Model Meso-NH to Analyze the Horns Rev 1 Wind Farm Photo Case

“…An underrepresentation of the clouds can be noticed: the sea smoke recognizable on the photo is not reproduced, even if high values of relative humidity are reached. Figures 12B, 13B are synthetic images of atmospheric fields (temperature, pressure, liquid and water vapor), simulated by the open source htrdr Monte Carlo code (Villefranque et al, 2019). To render these realistic cloud scenes, a virtual camera is placed approximatively at the positions of the camera of Christian Steiness when he took the photos (Figures 12A, 13A).…”

Monte-Carlo and Domain-Deformation Sensitivities

“…We shall focus here on one selected design parameter π, selected in relation to the geometry. It has been shown in [5] that when a function is stated in an integral form [1,6], the use of the Monte-Carlo method to calculate its sensitivity to geometrical parameters often leads to formalization and implementation difficulties. In this paper a new method to estimate geometrical sensitivity is presented which allows previously unsolvable configurations to be treated.…”