Evaluation and verification of different characterization methods of echo signal for the AEMS/ACDL

In view of the strong subjectivity in existing researches regarding the accurate extraction of the echo signal from the aerosol and carbon detection lidar (ACDL) onboard the atmospheric environment monitoring satellite (AEMS), a systematic evaluation of the pulse peak method (PPM), pulse integration method (PIM) and pulse fitting method (PFM) is conducted. Echo signals from four typical geographical regions, namely the plain, mountain, ocean and plateau, are analyzed to explore the applicability of different signal extraction methods under varying peak intensities and signal-to-noise ratios (SNRs). The differential absorption optical depth (DAOD) calculated by the PIM exhibits higher stability and smaller standard deviation than those obtained using the PPM and PFM, indicating that the PIM is more suitable for most geographical regions. In order to reduce the errors introduced by the uncertainties of meteorological data, a self-verification approach based on the micro-region stability assumption is proposed as the evaluation index of comparing the performance of three signal extraction schemes, providing a practical solution when the external verification data are unavailable. Computer simulations further demonstrate that the number of integration points for achieving the maximum SNR is inversely proportional to the mean SNR of the signal, offering a quantitative guidance for enhancing echo signal quality through appropriate integration points selection. The inversion accuracy of the XCO₂ obtained using different signal extraction methods is evaluated, and the PIM achieves the smallest XCO₂ deviation, with an average deviation of -0.43ppm compared with the XCO₂ data observed by the global total carbon column observing Network (TCCON) at the Xianghe station. This study provides a practical, efficient and robust technical framework for the XCO₂ inversion of the new generation lidar satellite AEMS together with offering the valuable technical support for the global CO₂ monitoring applications.