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Objective frequentist uncertainty quantification for atmospheric CO$_2$ retrievals by Patil, Pratik; Kuusela, Mikael; Hobbs, Jonathan is a scholarly article available to read on EtoBox.

What is Objective frequentist uncertainty quantification for atmospheric CO$_2$ retrievals about?

The steadily increasing amount of atmospheric carbon dioxide (CO$_2$) is affecting the global climate system and threatening the long-term sustainability of Earth's ecosystem. In order to better understand the sources and sinks of CO$_2$, NASA operates the Orbiting Carbon Observatory-2 & 3 satellites to monitor CO$_2$ from space. These satellites make passive radiance measurements of the sunlight reflected off the Earth's surface in different spectral bands, which are then inverted in an ill-posed inverse problem to obtain estimates of the atmospheric CO$_2$ concentration. In this work, we propose a new CO$_2$ retrieval method that uses known physical constraints on the state variables and direct inversion of the target functional of interest to construct well-calibrated frequentist confidence intervals based on convex programming. We compare the method with the current operational retrieval procedure, which uses prior knowledge in the form of probability distributions to regularize the problem. We demonstrate that the proposed intervals consistently achieve the desired frequentist coverage, while the operational uncertainties are poorly calibrated in a frequentist sense both at in

Author

Patil, Pratik; Kuusela, Mikael; Hobbs, Jonathan

Published

2020

Language

EN