Johanna Tamminen | Searcharxiv

arXiv2025

Scattering graph method for 3D radiative transfer

A novel method for monochromatic scalar 3D radiative transfer, designed primarily for modeling remote sensing imaging, is presented. For simulating an observation of an imaging satellite instrument, the method uses a heuristic scattering coupling function to model the inter-pixel scattering of radiation, which is represented with a graph. The GPU-capable code implementation of the method, TURSCA, was validated against two established 3D RT models, Siro and SHDOM with relative agreement at 3% and 6%, respectively. The capabilities of TURSCA in modeling a satellite observation of an emission plume are examined. The presented method opens up new avenues of research, especially in satellite-based remote sensing of atmospheres.

physics.comp-ph↗

arXiv2018

Likelihood informed dimension reduction for inverse problems in remote sensing of atmospheric constituent profiles

We use likelihood informed dimension reduction (LIS) (T. Cui et al. 2014) for inverting vertical profile information of atmospheric methane from ground based Fourier transform infrared (FTIR) measurements at Sodankylä, Northern Finland. The measurements belong to the word wide TCCON network for greenhouse gas measurements and, in addition to providing accurate greenhouse gas measurements, they are important for validating satellite observations. LIS allows construction of an efficient Markov chain Monte Carlo sampling algorithm that explores only a reduced dimensional space but still produces a good approximation of the original full dimensional Bayesian posterior distribution. This in effect makes the statistical estimation problem independent of the discretization of the inverse problem. In addition, we compare LIS to a dimension reduction method based on prior covariance matrix truncation used earlier (S. Tukiainen et al. 2016).

stat.CO↗