Abstract
Integrated-path differential absorption (IPDA) LiDAR is a promising means of measuring the global distributions of the column weighted xCO2 (dry-air mixing ratio of CO2) with adequate accuracy and precision. Most IPDA LiDARs are incapable of discerning the vertical information of CO2 diffusion, which is of great significance for studies on the carbon cycle and climate change. Hence, we developed an inversion method using the constrained linear least-squares technique for a pulsed direct-detection multi-wavelength IPDA LiDAR to obtain sliced xCO2. In the proposed inversion method, the atmosphere is sliced into three different layers, and the xCO2 of those layers is then retrieved using the constrained linear least-squares technique. Assuming complete knowledge of the water vapor content, the accuracy of the retrieved sliced xCO2 could be as high as 99.85% when the signal-to-noise ratio of central wavelength retrievals is higher than 25 (with a log scale). Further experiments demonstrated that different carbon characteristics can be identified by the sign of the carbon gradient of the retrieved xCO2 between the ABL (atmospheric boundary layer) and FT (free troposphere). These results highlight the potential applications of multiple wavelength IPDA LiDAR.
Highlights
At present, nearly half of the total anthropogenic CO2 emission has been offset by unidentified CO2 sinks [1]
Effect of Signal-to-Noise Ratio signal-to-noise ratio (SNR) of received signals of wavelength λ can be expressed by Equation (12)
One can conclude from Equation (12) that SNRλ is highly related to the hardware configuration
Summary
Nearly half of the total anthropogenic CO2 emission has been offset by unidentified CO2 sinks [1]. Dense observations of column xCO2 (dry-air mixing ratio of CO2), supplementing the existing ground-based CO2 monitoring networks, are urgently needed to provide stronger constraints on inversions of carbon fluxes using the atmospheric inversion technique [2]. Since 2000, several satellite-based sensors, such as AIRS, SCIAMACHY, and IASI, have been sent into orbit and are able to obtain data of XCO2 (column-average xCO2). Some dedicated greenhouse gas monitoring missions, including GOSAT, OCO-2, and TanSat, have successfully obtained XCO2 products with higher accuracy and precision than before. It is widely witnessed that these data are useful in deepening our understanding of the carbon cycle process, despite their susceptibility to aerosols and dependency on sunlight as well as insufficient coverage over high-latitude zones [3,4]
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