Extracting atmospheric profiles from hyperspectral data using particle filters

Atmospheric profiles of temperature and water vapor mixing ratio can be estimated from hyperspectral measurement of radiation intensity from the atmosphere. Current estimation methods rely on iteration and linearization to invert the non-linear radiative transfer model which relates atmospheric profiles to radiation intensity. Model inversion and atmospheric profile estimation can be accomplished with a particle filter, despite the large number of dimensions in the system state, by drawing particles toward observations in the proposal density.