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Abstract An operational suite of ground-based, remote sensing retrievals for producing cloud microphysical properties is described, assessed, and applied to 1 yr of observations in the Arctic. All measurements were made in support of the Surface Heat Budget of the Arctic (SHEBA) program and First International Satellite Cloud Climatology Project Regional Experiment (FIRE) Arctic Clouds Experiment (ACE) in 1997–98. Retrieval techniques and cloud-type classifications are based on measurements from a vertically pointing 35-GHz Doppler radar, microwave and infrared radiometers, and radiosondes. The retrieval methods are assessed using aircraft in situ measurements from a limited set of case studies and by intercomparison of multiple retrievals for the same parameters. In all-liquid clouds, retrieved droplet effective radii Re have an uncertainty of up to 32% and liquid water contents (LWC) have an uncertainty of 49%–72%. In all-ice clouds, ice particle mean sizes Dmean can be retrieved with an uncertainty of 26%–46% while retrieved ice water contents (IWC) have an uncertainty of 62%–100%. In general, radar-only, regionally tuned empirical power-law retrievals were best suited among the tested retrieval algorithms for operational cloud monitoring at SHEBA because of their wide applicability, ease of use, and reasonable statistical accuracy. More complex multisensor techniques provided a moderate improvement in accuracy for specific case studies and were useful for deriving location-specific coefficients for the empirical retrievals but were not as frequently applicable as the single sensor methods because of various limitations. During the yearlong SHEBA program, all-liquid clouds were identified 19% of the time and were characterized by an annual average droplet Re of 6.5 μm, LWC of 0.10 g m−3, and liquid water path of 45 g m−2. All-ice clouds were identified 38% of the time with an annual average particle Dmean of 73 μm, IWC of 0.014 g m−3, and ice water path of 30 g m−2.
Shupe et al. (Sat,) studied this question.