Dataset Information

Toward High Precision XCO₂ Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements.

ABSTRACT: TanSat is the 1st Chinese carbon dioxide (CO₂) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO₂ 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 O₂ A band retrieval. Accordingly, we extend the previous TanSat single CO₂ weak band retrieval to a combined O₂ A and CO₂ 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 XCO₂ retrieval error. Twenty sites of the Total Column Carbon Observing Network (TCCON) have been selected to validate our new approach for the TanSat XCO₂ retrieval. We show that our new approach produces a significant improvement on the XCO₂ 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 XCO₂ retrieval from TanSat and subsequently the Level-2 data production, and hence will be applied in the TanSat operational XCO₂ processing.

SUBMITTER: Yang D

PROVIDER: S-EPMC7983077 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Publications

Toward High Precision XCO2 Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements.

Yang D D Boesch H H Liu Y Y Somkuti P P Cai Z Z Chen X X Di Noia A A Lin C C Lu N N Lyu D D Parker R J RJ Tian L L Wang M M Webb A A Yao L L Yin Z Z Zheng Y Y Deutscher N M NM Griffith D W T DWT Hase F F Kivi R R Morino I I Notholt J J Ohyama H H Pollard D F DF Shiomi K K Sussmann R R Té Y Y Velazco V A VA Warneke T T Wunch D D

Journal of geophysical research. Atmospheres : JGR 20201109 22

TanSat is the 1st Chinese carbon dioxide (CO2) measurement satellite, launched in 2016. In this study, the University of Leicester Full Physics (UoL-FP) algorithm is implemented for TanSat nadir mode XCO2 retrievals. We develop a spectrum correction method to reduce the retrieval errors by the online fitting of an 8th order Fourier series. The spectrum-correction model and its a priori parameters are developed by analyzing the solar calibration measurement. This ...[more]

PMID: 33777605

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Project description:Advances in satellite retrieval of aerosol type can improve the accuracy of near-surface air quality characterization by providing broad regional context and decreasing metric uncertainties and errors. The frequent, spatially extensive and radiometrically consistent instantaneous constraints can be especially useful in areas away from ground monitors and progressively downwind of emission sources. We present a physical approach to constraining regional-scale estimates of PM2.5, its major chemical component species estimates, and related uncertainty estimates of chemical transport model (CTM; e.g., the Community Multi-scale Air Quality Model) outputs. This approach uses ground-based monitors where available, combined with aerosol optical depth and qualitative constraints on aerosol size, shape, and light-absorption properties from the Multi-angle Imaging SpectroRadiometer (MISR) on the NASA Earth Observing System's Terra satellite. The CTM complements these data by providing complete spatial and temporal coverage. Unlike widely used approaches that train statistical regression models, the technique developed here leverages CTM physical constraints such as the conservation of aerosol mass and meteorological consistency, independent of observations. The CTM also aids in identifying relationships between observed species concentrations and emission sources. Aerosol air mass types over populated regions of central California are characterized using satellite data acquired during the 2013 San Joaquin field deployment of the NASA Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) project. We investigate the optimal application of incorporating 275 m horizontal-resolution aerosol air-mass-type maps and total-column aerosol optical depth from the MISR Research Aerosol retrieval algorithm (RA) into regional-scale CTM output. The impact on surface PM2.5 fields progressively downwind of large single sources is evaluated using contemporaneous surface observations. Spatiotemporal R 2 and RMSE values for the model, constrained by both satellite and surface monitor measurements based on 10-fold cross-validation, are 0.79 and 0.33 for PM2.5, 0.88 and 0.65 for NO3 -, 0.78 and 0.23 for SO4 2-, and 1.01 for NH+, 0.73 and 0.23 for OC, and 0.31 and 0.65 for EC, respectively. Regional cross-validation temporal and spatiotemporal R2 results for the satellite-based PM2.5 improve by 30 % and 13 %, respectively, in comparison to unconstrained CTM simulations and provide finer spatial resolution. SO4 2- cross-validation values showed the largest spatial and spatiotemporal R2 improvement, with a 43 % increase. Assessing this physical technique in a well- instrumented region opens the possibility of applying it globally, especially over areas where surface air quality measurements are scarce or entirely absent.

Dataset Information

Toward High Precision XCO₂ Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements.

Publications

Toward High Precision XCO<sub>2</sub> Retrievals From TanSat Observations: Retrieval Improvement and Validation Against TCCON Measurements.

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