The Vertical Structure of Liquid Water Content in Shallow Clouds as Retrieved From Dual-Wavelength Radar Observations

• Published in: Journal of Geophysical Research: Atmospheres
• Main Authors: Zhu, Zeen, Lamer, Katia, Kollias, Pavlos, Clothiaux, Eugene E.
• Language: unknown
• Published: 2021
• Subjects: 54 ENVIRONMENTAL SCIENCES; North Atlantic
• Online Access: http://www.osti.gov/servlets/purl/1802498; https://www.osti.gov/biblio/1802498; https://doi.org/10.1029/2019jd031188

Observations collected over 3 months by the beam-matched second-generation Ka/W band Scanning Cloud Radar located at the Atmospheric Radiation Measurement Program Eastern North Atlantic observatory are used to advance existing liquid water content (LWC) retrieval techniques, quantify retrieval uncertainty, and subsequently characterize the impact of cloud dynamics and rain rates on the vertical distribution of LWC in boundary layer clouds both precipitating and broken. A threefold technique is proposed that involves (1) temporally averaging measured radar reflectivities collected at two wavelengths to 30-s resolution, (2) smoothing via fitting a second-degree polynomial to their dual-wavelength ratios within 187.5-m vertical overlapping sliding windows, and (3) averaging the multiple LWC estimates produced at each height. It is estimated that this technique reduced LWC retrieval uncertainty to 0.10–0.65 g/m 3 , depending on cloud thickness. Although individual retrievals remained noisy, statistics on subgroups of height-normalized LWC profiles show that, on average, the vertical distributions of LWC in most of the observed clouds followed a linear relationship with a degree of adiabaticity ranging from 0.6 to 0.2 for 200- to 600-m thick clouds. However, nonlinear LWC profiles were present in subgroups of cloud segments presenting intense (0.1–0.5 mm/hr) drizzle rates where LWC was observed to pool near cloud base and in subgroups of cloud segments within strong (0.6 m/s) downdrafts near cloud top where LWC was coincidently reduced. This nonlinearity is inconsistent with the use of adiabatic cloud assumptions for process studies and supports further development of retrievals like the one proposed.