Uncertainty Characterization and Propagation in the Community Long-Term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS)

Abstract

The Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS) retrieves multiple Essential Climate Variables (ECV) about the vertical atmosphere from hyperspectral infrared measurements made by the Atmospheric InfraRed Sounder (AIRS, 2002−present) and its successor, the Cross-track Infrared Sounder (CrIS, 2011−present). CLIMCAPS ECVs are profiles of temperature and water vapor, column amounts of greenhouse gases (CO2, CH4), ozone (O3) and precursor gases (CO, SO2) as well as cloud properties. AIRS (and CrIS) spectral measurements are highly correlated signals of many atmospheric state variables. CLIMCAPS inverts an AIRS (and CrIS) measurement into a set of discrete ECVs by employing a sequential Bayesian approach in which scene-dependent uncertainty is rigorously propagated. This not only linearizes the inversion problem but explicitly accounts for spectral interference from other state variables so that the correlation among ECVs (and their uncertainty) may be minimized. Here, we outline the CLIMCAPS retrieval methodology with specific focus given to its sequential scene-dependent uncertainty propagation system. We conclude by demonstrating continuity in two CLIMCAPS ECVs across AIRS and CrIS so that a long-term data record may be generated to study the feedback cycles characterizing our climate system.

Keywords

• climate data records
• hyperspectral
• satellite soundings
• inversion
• optimal estimation
• uncertainty propagation
• homogenization
• harmonization
• climate feedback cycles