PNNL

Abstract

Drizzle, a common feature of marine boundary layer clouds formed through collision-coalescence, 31 plays a key role in cloud microphysics and evolution. Yet, simultaneously retrieving cloud and 32 drizzle properties from remote-sensing observations remains challenging because drizzle droplets 33 often dominate radar signals, masking cloud contributions. To address this, we developed 34 ENCORE, a retrieval framework that combines shortwave radiometer, lidar, and cloud radar 35 measurements to estimate cloud and drizzle properties concurrently. Evaluation against in situ and 36 ground-based datasets at the Atmospheric Radiation Measurement (ARM) Eastern North Atlantic 37 (ENA) site demonstrates robust performance for cloud properties, with mean biases of 3–44% for 38 droplet number concentration, –21% to 19% for effective radius, and –54% to 69% for liquid water 39 content. Column-integrated quantities, including liquid water path and optical depth, differ by 10–40 50% and –20% to 33%, respectively, yielding radiation closure within 15%. ENCORE also 41 outperforms the ARM NDROP product in retrieving cloud droplet number concentration. Drizzle 42 retrievals, however, remain more challenging, with drizzle water content biases ranging from –43 87% to –56%. Drizzle number concentration is especially uncertain under weak drizzle conditions, 44 likely due to sensitivity limitations and radar thresholds used in ENCORE. Despite these 45 challenges, process-based evaluations, such as Z–R relationships and cloud adiabaticity, are 46 consistent with in situ observations, demonstrating ENCORE’s ability to capture cloud–drizzle 47 covariability that is critical for understanding warm-rain processes and aerosol–cloud–48 precipitation interactions.