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Challenges in Retrieving Liquid Clouds and Precipitation Data: A Prior Datasets Review

This summary examines the challenges in classifying clouds, determining precipitation, turbulence, and Doppler spectrum skewness for liquid clouds. It delves into dataset variability, droplet relations, and spatial distributions. The text discusses the Melpitz campaign's reflective calculations and compares them with cloud radar data. It explores available datasets, in-situ methods, and technological sensitivities in retrieving liquid cloud data.

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Challenges in Retrieving Liquid Clouds and Precipitation Data: A Prior Datasets Review

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  1. Breakoutsessing „The liquid people“ Summary about Prior datasetsfor liquid clouds/precipitationretrievals

  2. Prior datasetsfor liquid cloudsbesides Z=a LWCbrelationships Challenges: Classificationofcloudsbeforeapplyingconditionalretrieval? Difficultytodeterminewhetheror not precipitatingcloud (dBZthreshold?) Degreeofturbulence, entrainmentatcloud top, mixingwithincloud? Degreesoffreedomdeterminedbymodelcomplexity DSD byaircraftobservationsoften a meanover a longdistance Skewnessofdopplerspectrumdeterminesclouddroplet/drizzlerelation

  3. Prior datasetsfor liquid cloudsbesides Z=a LWCbrelationships Variabilityof a and b withinonecloud? Can a and b beadjusted? Meanprofile? spatialdistributionofdroplets? no uniform distribution, bigimpactofscattering conditionalsamplingof DSD Melpitzcampaignwith ACTOS in September 2013 calculatedreflectivityfrom in-situ probes (ACTOS) comparedtoreflectivityfromcloudradar

  4. Prior datasetsfor liquid cloudsbesides Z=a LWCbrelationships Datasets available? in situ: towers, aircrafts Ballooncampaign -> verticaldistribution Dual wavelengthradar -> LWC directly, howeververy sensitive toanyothererrors, e.g. pointing (beam matching), smalldifferences in reflectivityhavetobedetected. Availableat ARM, Chilbolton! LES model (Cabauwcontinuouslyrunningfor 1.5 years) LES outputasconstraintforretrievals

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