The ACOS CO<sub>2</sub> retrieval algorithm – Part 1: Description and validation against synthetic observations

Abstract

This work describes the NASA Atmospheric CO₂ Observations from Space (ACOS) <i>X</i>_CO₂ retrieval algorithm, and its performance on highly realistic, simulated observations. These tests, restricted to observations over land, are used to evaluate retrieval errors in the face of realistic clouds and aerosols, polarized non-Lambertian surfaces, imperfect meteorology, and uncorrelated instrument noise. We find that post-retrieval filters are essential to eliminate the poorest retrievals, which arise primarily due to imperfect cloud screening. The remaining retrievals have RMS errors of approximately 1 ppm. Modeled instrument noise, based on the Greenhouse Gases Observing SATellite (GOSAT) in-flight performance, accounts for less than half the total error in these retrievals. A small fraction of unfiltered clouds, particularly thin cirrus, lead to a small positive bias of ~0.3 ppm. Overall, systematic errors due to imperfect characterization of clouds and aerosols dominate the error budget, while errors due to imperfect meteorology, surface reflectance, and radiative transfer assumptions are small

A method for colocating satellite &lt;i&gt;X&lt;/i&gt;&lt;sub&gt;CO&lt;sub&gt;2&lt;/sub&gt;&lt;/sub&gt; data to ground-based data and its application to ACOS-GOSAT and TCCON

“…The ACOS-GOSAT data processing algorithm is based on the optimal estimation approach of Rodgers (2000) and is described in detail in O'Dell et al (2012). It is modified from the OCO retrieval algorithm (Bosch et al, 2006;Connor et al, 2008;Boesch et al, 2011) to account for the different physical viewing geometries and properties such as instrument line shapes and noise models.…”

Bias corrections of GOSAT SWIR XCO&lt;sub&gt;2&lt;/sub&gt; and XCH&lt;sub&gt;4&lt;/sub&gt; with TCCON data and their evaluation using aircraft measurement data

“…Consequently, several studies have described bias corrections of the satellite retrieval data by using linear regression (e.g., Wunch et al, 2011b;Cogan et al, 2012;Guerlet et al, 2013;Schneising et al, 2013;Nguyen et al, 2014). Wunch et al (2011b) have attempted to correct spatially and temporally varying biases in the Atmospheric CO 2 Observations from Space retrievals of the GOSAT (ACOS-GOSAT; O'Dell et al, 2012;Crisp et al, 2012) data obtained over land using an empirical linear regression model with which they correlated variabilities in XCO 2 retrievals with surface albedo, the difference between the retrieved and a priori surface pressure, airmass, and the oxygen A-band spectral radiance. They used the GOSAT data in the Southern Hemisphere as the reference values for the linear regression and evaluated the bias correction against the Total Carbon Column Observing Network (TCCON) data from the Northern Hemisphere.…”

Simulated retrievals for the remote sensing of CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, CO, and H&lt;sub&gt;2&lt;/sub&gt;O from geostationary orbit

“…This method for retrieval has been widely used for GOSAT (Yoshida et al, 2011), Tropospheric Emission Spectrometer, and OCO-2 (Kuang et al, 2002;Connor et al, 2008;O'Dell et al, 2012;Crisp et al, 2012). The state vector x, model parameters b, and error e are related to the measurement vector y by a forward model F:…”

“…Aerosol and cloud contamination are substantial sources of systematic errors in trace gas retrievals (e.g., Dufour and Bréon, 2003;Houweling et al, 2005;Butz et al, 2011;O'Dell et al, 2012;Merrelli et al, 2015;Zhang et al, 2015). Novel cloud screening algorithms, such as the one designed for the OCO-2 mission, are able to screen out most of the scenes with thick clouds and aerosols.…”