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

Nadia Smith,Christopher D Barnet

doi:10.3390/rs11101227

Nadia Smith, Christopher D Barnet

Open Access

https://doi.org/10.3390/rs11101227

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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.

Highlights

Modern-era hyperspectral infrared sounders measure emitted radiance at the Top Of Atmosphere (TOA) in hundreds of narrow spectral channels
The Bayesian Optimal Estimation (O-E) inversion framework is widely accepted by the scientific community as the most suitable for retrieving science quality products because it allows uncertainty propagation per datum [1,2,3,4,5,6,7,8]. (We use the term “datum” here to refer to an individual Level 2 retrieval footprint.) Despite a good framework, the retrieval of multiple discrete Essential Climate Variables (ECVs; [9]) from sounding measurements for climate applications remains exceedingly complex
We introduce the Community Long-term Infrared Microwave Coupled Atmospheric Product System (CLIMCAPS) that we designed to retrieve ECVs from a multi-decadal harmonized record of the Atmospheric Infrared Sounder (AIRS, in low-Earth orbit since 2002) and Cross-track Infrared Sounder (CrIS, in a similar orbit since 2011) for climate applications

Summary

Introduction

Modern-era hyperspectral infrared sounders measure emitted radiance at the Top Of Atmosphere (TOA) in hundreds of narrow spectral channels. These include profiles of temperature, water vapor, greenhouse and pollutant gases (O3, CO, CH4, SO2, HNO3, and N2O), cloud top pressure, cloud fraction, surface temperature and emissivity as well as an array of diagnostic metrics that characterizes scene complexity and retrieval quality Unlike these legacy systems, CLIMCAPS adopts a novel scene-dependent uncertainty propagation scheme to retrieve ECVs that are largely independent of each other and the background atmospheric state. 2019, 11, 1227 an overview of the modern-era hyperspectral infrared instruments on low-earth orbiting platforms (Section 2.1) followed by a generalized overview of the O-E framework (Section 2.2.1) and how we adopted it to achieve scene-dependent uncertainty propagation in CLIMCAPS (Section 2.2.2) This is followed by a discussion of our choice for a priori estimates of temperature and moisture (Section 2.2.3) with a discussion of retrievals in cloudy scenes (Section 2.2.4). We conclude with a summary and discussion of future work (Section 4)

Hyperspectral Infrared Sounders

Bayesian Optimal-Estimation

Scene-Dependent Uncertainty Propagation

Prior Estimate of Atmospheric State

Datum-Specific Uncertainty Metrics