Impact of Aerosol Property on the Accuracy of a CO2 Retrieval Algorithm from Satellite Remote Sensing

Abstract

Based on an optimal estimation method, an algorithm was developed to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO 2 ) using Shortwave Infrared (SWIR) channels, referred to as the Yonsei CArbon Retrieval (YCAR) algorithm. The performance of the YCAR algorithm is here examined using simulated radiance spectra, with simulations conducted using different Aerosol Optical Depths (AODs), Solar Zenith Angles (SZAs) and aerosol types over various surface types. To characterize the XCO 2 retrieval algorithm, reference tests using simulated spectra were analysed through a posteriori XCO 2 retrieval errors and averaging kernels. The a posteriori XCO 2 retrieval errors generally increase with increasing SZA. However, errors were found to be small (<1.3 ppm) over vegetation surfaces. Column averaging kernels are generally close to unity near the surface and decrease with increasing altitude. For dust aerosol with an AOD of 0.3, the retrieval loses its sensitivity near the surface due to the influence of atmospheric scattering, with the peak of column averaging kernels at~800 hPa. In addition, we performed a sensitivity analysis of the principal state vector elements with respect to XCO 2 retrievals. The reference tests with the inherent error of the algorithm showed that overall XCO 2 retrievals work reasonably well. The XCO 2 retrieval errors with respect to state vector elements are shown to be <0.3 ppm. Information on aerosol optical properties is the most important factor affecting the XCO 2 retrieval algorithm. Incorrect information on the aerosol type can lead to significant errors in XCO 2 retrievals of up to 2.5 ppm. The XCO 2 retrievals using the Thermal and Near-infrared Sensor for carbon Observation (TANSO)-Fourier Transform Spectrometer (FTS) L1B spectra were biased by 2.78˘1.46 ppm and 1.06˘0.85 ppm at the Saga and Tsukuba sites, respectively. This study provides important information regarding estimations of the effects of aerosol properties on the CO 2 retrieval algorithm. An understanding of these effects can contribute to improvements in the accuracy of XCO 2 retrievals, especially combined with an aerosol retrieval algorithm.

Quantification of uncertainties in OCO-2 measurements of XCO&lt;sub&gt;2&lt;/sub&gt;: simulations and linear error analysis

“…Characterizing the uncertainties in XCO 2 as measured, and in how these uncertainties vary in space and time, is critical for this purpose. Earlier studies of the error to be expected from such observations include Butz et al (2009) and Jung et al (2016). The present study is part of the ongoing effort at uncertainty quantification for the OCO-2 mission.…”

Retrieving XCO2 from GOSAT FTS over East Asia Using Simultaneous Aerosol Information from CAI

“…At each iteration, the validity of the updated state vector is evaluated by determining if the cost function has decreased. The iteration is finished when the state vector reaches convergence [19,36]. Finally, X CO2 is calculated using the pressure weighting function h, which is defined in O'Dell et al [20], and with the CO 2 final state vectors, x f,CO2 , as…”

“…It is also well described in previous papers, therefore only the fundamental theory will be described in this chapter [19,36]. This optimal estimation method uses an a priori estimate of each state vector (a parameter to be retrieved in the iterative-inverse method) to constrain the retrieval and to find appropriate solutions.…”

A Dark Target Algorithm for the GOSAT TANSO-CAI Sensor in Aerosol Optical Depth Retrieval over Land

“…Although the challenges involved in developing an algorithm for retrieving AOD from GOSAT TANSO-CAI are considerable, such an algorithm is considered essential if precise aerosol information from the TANSO-CAI is ever to be obtained. It has the potential to reduce aerosol-related errors and improve the accuracy of TANSO-FTS data, and will also expand the scope of future aerosol-related research [33].…”