Detection and Height Measurement of Tenuous Aerosols and Clouds in CATS Lidar Data at High Resolution Using the CATS-Density-Dimension Algorithm
Journal Article
Overview
abstract
Tenuous atmospheric layers play an important part in applications ranging from modeling atmospheric heat to transportation safety, disaster and hazard response to wildfires and volcanic eruptions. The height and optical properties of atmospheric layers are usually provided by satellite lidar observations. A known challenge in the identification of atmospheric layers is the detection of tenuous clouds and aerosols, which often escape existing layer detection schemes. The problem lies in the discrimination of faint signals from background, especially in situations where stronger reflectors exist within the same regions, during nighttime and daytime conditions. The Density-Dimension-Algorithm (DDA) is an autoadaptive algorithm which has had wide application from the detection of crevasses on ice sheets in ICESat-2 ATLAS data, to the detection of atmospheric layers in ICESat-2 and CALIPSO lidar data. This paper focuses on the implementation of the DDA for the CLoud-Aerosol Transport System (CATS) instrument aboard the International Space Station (ISS). We aim to demonstrate the versatility of the DDA in detecting a variety of atmospheric layers, with an emphasis on tenuous clouds and aerosols. Layer detection facilitated by the new CATS-DDA is evaluated using airborne data from collected with the Cloud Physics Lidar (CPL). Comparisons of CATS-DDA results with detections in the CATS Level 2 Feature Mask product indicate an increased detection capability for tenuous layers and a refined, more realistic representation of atmospheric layers afforded by the high-resolution capabilities of the DDA, which retains the native resolution of the recorded data. Beyond the functionality of the DDA for layer detection in CATS lidar data, results from this paper serve to demonstrate the ability of the DDA to overcome some of the limitations of the CATS instrument. These findings may aid in developing instruments and algorithms for future atmospheric satellite missions, such as NASA FALCON (Fleet for the Atmosphere Linking Commercial Observations with NASA).
