Based on an optimal estimation method, an algorithm was developed to retrieve the column-averaged dry-air mole fraction of carbon dioxide (XCO2) 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 XCO2 retrieval algorithm, reference tests using simulated spectra were analysed through a posteriori XCO2 retrieval errors and averaging kernels. The a posteriori XCO2 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 XCO2 retrievals. The reference tests with the inherent error of the algorithm showed that overall XCO2 retrievals work reasonably well. The XCO2 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 XCO2 retrieval algorithm. Incorrect information on the aerosol type can lead to significant errors in XCO2 retrievals of up to 2.5 ppm. The XCO2 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 CO2 retrieval algorithm. An understanding of these effects can contribute to improvements in the accuracy of XCO2 retrievals, especially combined with an aerosol retrieval algorithm.
Bibliographical noteFunding Information:
This work was supported by National Institute of Meteorological Sciences (NIMS) Research Grant "Development and Application of Methodology for Climate Change Prediction" and the Eco Innovation Program of Korea Environmental Industry & Technology Institute (KEITI, 2012000160002). The authors appreciate the GOSAT Science team of NIES and JAXA Earth Observation Research Center (EORC), Japan, and Yukio Yoshida, Akihiko Kuze and Tatsuya Yokota in particular for useful discussions and immeasurable help with this work. We also appreciate TCCON for providing FTS data obtained from the TCCON Data Archive, operated by the California Institute of Technology, and Shuji Kawakami and Isamu Morino for the use of TCCON dataset at Saga and Tsukuba.
© 2016 by the authors.
All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)