Concurrent Cloud and Drizzle Observations From Synergistic Ground‐Based Measurements

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

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