Bayesian inference on integrated continuity fluid flows and their application to dust aerosols

The significant role dust aerosols play in the earth's climate system and microbial nutrition cycles have lead to increased efforts in employing remote sensing to monitor their genesis, transport and deposition. In this contribution we considerably refine our earlier statistical models for aerosol detection and atmospheric transport that rely on latent Gaussian Markov random fields for inference. Based on explicitly satisfying the so-called integrated continuity equation we develop a Bayesian generalized linear model intrinsically expressing the divergence of the field as a multiplicative factor covering physical aspects such as compressibility and column projection. Alongside employing surface emissivity estimates for improved genesis detection, we conduct a simulation study clearly showing a reduction of errors in the estimated flow field. We conclude with a case study that relates this experimental finding back to a dust event over northern Africa.