<pre>Remote sensing is an important measurement technique when probing the atmosphere as it is rather flexible and <br />allows for the measurement of numerous variables. For example, spectrometers are commonly used to quantify the <br />concentrations of trace gases by recording spectra of direct or scattered sunlight. Due to scattering, the light <br />paths between sun and detector can be rather complicated and radiative transfer models are necessary to retrieve <br />the information contained in the spectra. <br />Since these spectrometer measurements have to be made in spectral regions with strong absorption, it is necessary <br />to model many wavelengths to reach a sufficiently accurate representation of the absorption lines and their effects <br />on the light paths. <br />Thus, 1D models are often implemented for a fast analysis of the recorded spectra. However, this approach assumes <br />horizontal homogeneity and local sources cannot be resolved. In contrast, 3D Monte Carlo models are more realistic <br />and are able to represent this inhomogeneity, but they are computationally expensive and are not suitable for <br />operational use.</pre>
<pre>We improve an existing Monte Carlo model by implementing efficient algorithms for the simultaneous calculation of <br />several wavelengths to decrease the required computation time. <br />Furthermore, we examine to which scatter order the 3D model provides more detailed results while maintaining a <br />reasonable run time.<br />This finally leads to a coupling of these two types of radiative transfer models via the scatter order into one <br />efficient model which performs realistic simulations at a computational cost comparable to 1D models.<br />So we are able to detect sources along the line of sight of ground-based measurements of scattered sunlight.<br />Here, we present our objective and first results.</pre>
PAH in Vectorized Three Dimensional Monte Carlo Dust Radiative Transfer Models
