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Cloud Water Characteristics and Spectral Analysis In Supercooled Environment: A Microphysical Study

Explore the cloud water properties in a supercooled environment with detailed spectral analysis revealing fluctuating liquid and ice peaks, turbulence impacts, riming features, and multi-peak phenomena. Gain insights on LWP, MDV, RS profiles, and wind speeds variations.

maggie-marquez
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Cloud Water Characteristics and Spectral Analysis In Supercooled Environment: A Microphysical Study

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  1. 2014-03-02, 10-11 UTC

  2. Supercooled liquid water

  3. Moments onlyfrom „principalpeak“; canchangebetween liquid andicepeakdepending on whichoneisstronger!

  4. Characteristicsofcloudwaterpeak: Ifturbulenceislow, peakisverynarrow (narrow PSD), closeto 0 m/s, oftenseparatedfromfasterfallingsmalliceandaggregates. Can beusedastracerforestimatingverticalairvelocity.

  5. Thicknessofsupercooledlayer at cloud top (usethe MDV switchingartefact!) seemstobewellcorrelatedwith LWP curve.

  6. The RS-RH profilesuggests a secondpossiblelayerofsupercooledwater at around 500m. Sinceturbulenceincreasesdrastically in thelowest 500m itis not easy to find a clear liquid peakhere. Maybethesizeandconcentrationofdropletsis just tosmall/lowtobedetected…

  7. Riming

  8. Starting at the liquid layerwealready find a peakbetween 0.5 and 1 m/s. This couldbe due tosmalliceandfirstaggregatesoralreadyrimedsinglecrystals (hardtosaywithoutotherparameters)

  9. Just a fewhundredmetersbelow, the dominant peakisalreadybetween 1 and 1.7 m/s whilewe find a large areaof different icevelocities. Those large Doppler velocitiesindicate additional riminghashappenedduringthe last fewhundredmeters

  10. Finally, at 500m, themainpeakspeedsupto 1.5 to 2 m/s, a clearrimingfeaturebecausevertical wind speedseemstobeingenerallow in thiscloud. Youcan also nicelyseethespeedingupofthespectra in therangespectrogram! Watch also thenicecorrelationbetween LWP maximaand MDV maxima!

  11. Turbulence

  12. At 400m thespectralpeaksare still clearlyseparatedandrelativelynarrow. Also the temporal variabilityofthe MDV isrelativelysmall.

  13. A fewrangegatesbelow, theseparatedspectramoreandmoremergetoonepeak due toturbulencebroadening. The variations in MDV (see Time Spectrogram) are also muchstrongerandof larger „frequency“.

  14. At 275m aboveground, wecanonlyidentifyonebroadpeakandthevariations in MDV stronglyincreased.

  15. 2014-02-01, 11-12 UTC

  16. Cloud Structure– Fallstreaks – Size SortingEffects

  17. Unlikethecasebeforewe find forthiscloudtiltedfallstreaks. At cloud top the wind speedis larger than at e.g. 1km. Can thisexplainthetiltedfallstreaks?

  18. Formoredetails, seemy ERAD talk on Tuesday… Fallstreaksfor: Snowflake,vdop= 1 m/s Rimedsnow, vdop = 2 m/s Verticalprofileof horizontal wind u (unidirectional) Note: The time fortheparticleto fall 600m (600s) is not neccesarilyequaltothe time onewouldderivefromthefallstreakstructure (300s) !

  19. „Background“ ice/snowspectruminbetweenthefallstreaks

  20. Movingtotheleft, weentertheleftsideofthestreakwhereweexpectthe fastest particlestoappear (notetherightpeak in thespectrum!)

  21. Movingfurthertotheleft, the fastest particlesdisappear, andthe fast peakseemstomergewiththebackgroundspectrum

  22. Super-cooled liquid water

  23. RS indicatesthicklayer at cloud top with 100% RH. Withinthislayerwecanoftenseethenarrow liquid peak. LWP andplumestructureseemtobecorrelated.

  24. The HSRL lidardatashowthat liquid wateralreadyexistswhenthe RS firstreaches 100% (2km). However, thedropletsizesand/orconcentrationsseemtobelowandthusthe liquid peakisveryweak.

  25. Riming

  26. At the top ofthefallstreaks/plumestheparticlesarealready (at least partly) rimed

  27. But also outside thefallstreaks, thespectrarevealrimedparticlesbelowthethick liquid layer.

  28. Multi-peak Spectra

  29. Itis not trivial tosaywherethe multiple peakscomefromsincetheyareinfluencedbydynamicaleffects (e.g. mergingoftwofallstreaks) aswellasbymicrophysicaleffects (e.g. riming).

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