Toward High Precision XCO₂Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements

Abstract

TanSat is the 1st Chinese carbon dioxide (CO 2) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO 2 retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8 th order Fourier series. The spectrum-correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This correction provides a significant improvement to the O 2 A band retrieval. Accordingly, we extend the previous TanSat single CO 2 weak band retrieval to a combined O 2 A and CO 2 weak band retrieval. A Genetic Algorithm (GA) has been applied to determine the threshold values of post-screening filters. In total, 18.3% of the retrieved data is identified as high quality compared to the original measurements. The same quality control parameters have been used in a footprint independent multiple linear regression bias correction due to the strong correlation with the XCO 2 retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO 2 retrieval. We show that our new approach produces a significant improvement on the XCO 2 retrieval accuracy and precision when compared to TCCON with an average bias and RMSE of −0.08 ppm and 1.47 ppm, respectively. The methods used in this study can help to improve the XCO 2 retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO 2 processing.

The First Global Carbon Dioxide Flux Map Derived from TanSat Measurements

“…The O 2 A band and CO 2 weak band measurements have been used together in new retrievals that correct the parameters of water vapor, surface pressure, temperature, aerosol, cirrus, and instrument model. A genetic algorithm was used in post screening, and then a multiple linear regression model was applied to correct bias (Yang et al, 2020). In this study, we used a 15-month (March 2017-May 2018) XCO 2 retrieval from TanSat ND measurements.…”

“…The first global XCO 2 map measured by TanSat was reported in a previous study (Yang et al, 2018), and Total Column Carbon Observing Network validation shows 2.2 ppm accuracy for version 1 of the TanSat L2 data product (Liu et al, 2018). The accuracy and precision of TanSat XCO 2 retrievals were further improved using a wavelength dependence gain factor to correct the spectrum continuum feature (Yang et al, 2020). A new version of TanSat XCO 2 was recently released to the public (Yang et al, 2021), which provides an opportunity to improve our knowledge of global carbon flux from flux inversions based on TanSat measurements.…”

The Significance of Fast Radiative Transfer for Hyperspectral SWIR XCO2 Retrievals

“…The UoL algorithm was previously described by Cogan et al [24], Yang et al [25], Somkuti [36], however we summarize the main aspects that are of relevance to this work. Very similar to the ACOS and RemoTeC algorithms, the UoL algorithm considers a model atmosphere of horizontally stratified layers in which all physical properties, such as temperature and humidity, are homogeneous within each layer.…”

“…While e.g., Nelson et al [22] have explored the possibility of applying a clear-sky only retrieval, in which atmospheric scattering is not considered, they found that scene selection and post-retrieval filtering must be performed much more aggressively to keep retrieval errors low. As such, the preferred option at the time being is to include atmospheric scattering in contemporary XCO 2 retrieval algorithms, such as RemoTeC [23], the University of Leicester (UoL) algorithm [24,25], the NIES algorithm [26], WMF-DOAS [27,28], FOCAL [29,30], and the NASA Atmospheric CO 2 Observations from Space (ACOS) algorithm [31].…”

GFIT3: a full physics retrieval algorithm for remote sensing of greenhouse gases in the presence of aerosols

“…Many different full physics retrieval algorithms, which explicitly account for atmospheric absorption and scattering and surface reflection in the radiative transfer (RT) forward modeling, have been developed for spaceborne instruments for retrieving column-averaged dry air mole fractions of atmospheric carbon dioxide (XCO 2 ) and methane (XCH 4 ). Examples of these instruments include Orbiting Carbon Observatory-2 (OCO-2; Boesch et al, 2011;O'Dell et al, 2018;Reuter et al, 2017), the Greenhouse gases Observing SATellite (GOSAT; Bril et al, 2012;Butz et al, 2011;Yoshida et al, 2013), and TanSat (Wang et al, 2020;Yang et al, 2020). Full physics algorithms for retrieving GHGs explicitly fit aerosol optical and microphysical properties in order to minimize biases induced by scattering.…”