Remote sensing of atmospheric state variables typically relies on the inverse solution of the
radiative transfer equation. An adequately characterized retrieval provides information on …

This article sets forth a practical methodology for uncertainty quantification of physical state
estimates derived from remote sensing observing systems. Remote sensing instruments …

Remote sensing of atmospheric carbon dioxide (CO2) carried out by NASA's Orbiting
Carbon Observatory-2 (OCO-2) satellite mission and the related uncertainty quantification …

Estimates of atmospheric carbon dioxide concentration from satellites now span multiple
years and are providing information on key processes in the carbon cycle. Methodology for …

Markov Chain Monte Carlo (MCMC) is a powerful and promising tool for assessing the
uncertainties in the Orbiting Carbon Observatory 2 (OCO-2) satellite's carbon dioxide …

In the context of Gaussian process regression with functional inputs, it is common to treat the
input as a vector. The parameter space becomes prohibitively complex as the number of …

Remote sensing of atmospheric carbon dioxide (CO 2) carried out by NASA's Orbiting
Carbon Observatory-2 (OCO-2) satellite mission and the related Uncertainty Quantification …

Forecasting is an important area in statistics and as a result it is important that our forecasts
reflect our uncertainties. But most importantly, our forecasts should be as accurate as …