Background Signal Characterization Analysis for ACDL/DQ-1 at Multichannel of 532 nm

The Aerosol and Carbon Detection Lidar (ACDL) on board the Atmospheric Environment Monitoring Satellite (DQ-1) has been successfully operationalized, and it possesses the capability of detecting carbon dioxide, aerosols, and clouds around the world. However, in contrast to other spaceborne lidars currently/previously in operation (e.g., the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation-CALIPSO, the Ice, Cloud, and land Elevation Satellite-2 mission-ICESat-2 and so on), the ACDL does not incorporate a background signal monitor. Consequently, it is crucial to develop background signal acquisition algorithm for ACDL and to analyze the background characterization. In this paper, a three-segmented background signal acquisition algorithm is designed for ACDL to ensure that the acquired background signals match the instrument characteristics. Additionally, the multi-channel background signals measured by ACDL during daytime and nighttime are characterized in detail, including the features in single profile, along the orbit, on a monthly, semi-annually basis, and particular regions. The quantitative analysis of the data reveals a decrease in background signal intensity along latitudinal lines during nocturnal periods (<0.0005 V). Concurrently, elevated signal values are observed within specific regions of South Atlantic Anomaly (SAA). Additionally, the daytime background signal exhibits a high degree of sensitivity to the characteristics of the feature, and it is influenced by the relative positions of the Sun and the Earth. These findings corroborate the stability of the background signal and its consistency with the behavior of the background signal collected by CALIPSO. And the background removed signals collected by ACDL showed good agreement (difference less than 0.026 V) in the region of consistent diurnal aerosol loads (18-22 km) under clear-air conditions. The background acquisition algorithm and characterization analysis proposed in this paper provide accurate data and theoretical support for subsequent calibration and product inversion. Additionally, it offers a solution for background signal extraction in the case of spaceborne lidars that are not equipped with a background signal monitor.