Determining Cloud Particle Types During the IMPACTS Field Campaign Using Backscatter Lidar Data and a Clustering Approach

The role of clouds in Earth’s climate system is heavily dependent on their height, thickness, and particle properties. Determining cloud particle properties (phase, ice habit, etc.) is important to understanding cloud impacts on the radiation budget. Backscatter lidars, such as the Cloud Physics Lidar (CPL), are important tools for estimating cloud height, thickness, and cloud phase. CPL is a multi-wavelength (355, 532, 1064 nm) elastic backscatter lidar built for use on the high-altitude ER-2 aircraft. Recently CPL flights during the Investigation of Microphysics and Precipitation in Atlantic Coast Threatening Snowstorms (IMPACTS) project provide an excellent opportunity to improve cloud phase algorithms. IMPACTS consists of three 6-week deployments (2020, 2022, 2023) utilizing a complementary suite of remote-sensors on the ER-2 and in-situ instruments on the NASA P-3 (within the clouds). Lidar cloud phase algorithms have traditionally been limited to liquid water vs ice and classify phase on a cloud “layer” basis for each profile. Thus, they do not capture the vertical variability within a cloud. Here we present an agglomerative hierarchical clustering (“bottom-up”) approach performed on CPL IMPACTS vertically binned data (not layer-integrated values). This clustering technique better captures the vertical variability within cloud layers and enables finer identification of particle types (5 largest clusters are selected). Clusters are assigned particle types using three methods: (1) collocated in situ cloud measurements from the P-3 aircraft where possible, (2) associating cases when P-3 data is not present to clusters of similar properties that have a “known” particle type based on the P-3 observations, or (3) previous published papers relating lidar data to in situ cloud microphysical properties.