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Geometrical description of the hydrometeor images sampled

HAIC working document 22/10/2013 meeting. Geometrical description of the hydrometeor images sampled with the AIRBUS imager during the Cayenne flight 1423 Minimal model for the small ice particles shapes (D<100 µm) ? C. Duroure, A. Delplanque (LaMP), M. Weber (AIRBUS)

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Geometrical description of the hydrometeor images sampled

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  1. HAIC working document 22/10/2013 meeting Geometrical description of the hydrometeor images sampled with the AIRBUS imager during the Cayenne flight 1423 Minimal model for the small ice particles shapes (D<100 µm) ? C. Duroure, A. Delplanque (LaMP), M. Weber (AIRBUS) 1- We verify some (quite trivial) correlation between some geometrical variables. 1bis- we verify (for this simple case) the coherence of the automatic shape recognition 2- We observe some (not trivial) correlation between these geometrical variables and the bias of TWC measurements using hot wire probe and imaging probe. 3- We propose a statistical model of small ice crystals (D<100 µm) using the observed distribution of geometrical variables (aspect ratio and/or rugosity) 4- Work in progress, not presented: Link with the temperature and vertical velocity field, possible estimation of the “microphysical age” of the studied regions (few seconds after nucleation, few minutes ?) Technical trouble: 1-Why the correlation between TWC of hot wire probe and total concentration of images is better than the correlation using PSD and mass to diameter functions ? 2-Sensibility of the results to the threshold rules !

  2. AIRBUS Cayenne FL1423: « random circle fligth » in a active MCS region L1 L3 L2 L4 Case study of four legs (20 Km scale) into the one hour leg at 10.6 Km level (T=-42 °C) (Try this fligth strategy in the 2014 Darwin experiment ? Fixed level and multiple circle leg around active region)

  3. Perimeter of a equal surface disk / Perimeter of the image « Roughness » coefficient Aspect ratio ( Width / Lmax) Three different geometrical variables: In this case study it exist quite good correlations

  4. Aspect ratio ( Width / Lmax) ( 1hz , 200 meters average) L1 L2 L3 L4 100 Km Ratio between TWC from the imager and TWC from hot wire probe (Robust) How explain the correlation between the geometrical aspect ratio and TWC estimation with two different probes ?

  5. Aspect ratio ( Width / Lmax) ( 1hz , 200 meters average) L1 L2 L3 L4 100 Km Ratio between the probability of SPHEROID and SMALL IRREG. shapes Correlation between the geometrical aspect ratio and TWC the automatic shape recognition (for this fligth)

  6. Leg 2,4 Leg 1,3 Modelisation of small ice cristal (D<100µm) using observed mean aspect ratio distribution (or other geometrical variables) => random distribution of oblate spheroid with fixed distribution of aspect ratio Assuming: Density close to pure ice (0.89 Gr/Cm-3, B=3) Include not isometric aspect ratio evolution with size ? ( B<3)

  7. Mean diameter (µm) Ratio between TWC from the imager and TWC from hot wire probe (Robust) Correlation with the mean diameter ???

  8. Total concentration Ratio between TWC from the imager and TWC from hot wire probe (Robust) Correlation with the total concentration (and/or TWC) ???

  9. Vertical velocity (W) Temperature (Tp) Total concentration (Ct) Local anti-correlation W-Tp and Local correlation Ct-Tp

  10. 7 Km Hight concentration + hight aspect-ratio ( + temperature jump) => new nucleation region ?

  11. Automatic shape recognition procedure definition Ref: ThèseRémy Lefevre, LaMP, Mars 2007 n°:527

  12. Technical troubles : What is the better treshold ? Use of VAL ? Valid images All images New GEO file Old GEO file GEO file low tresh.

  13. Technical trouble: the result for the new version of the V1423_GEO file are very sensible to the VAL (validity) variable.

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