Best estimation of a local mass to diameter function using imaging probe and hot-wire TWC measurement Case of the AIRBUS imager during the Cayenne flight 1423C. Duroure, A. Delplanque (LaMP), M. Weber (AIRBUS)

1- We present a simple method to find the best mass to diameter function for a given sample set of hydrometeor images using as reference a alternate TWC measurement (in this case the hot wire Robust probe)2- We compare the TWC estimation using this method with other methods (method using only the total count, methods using classical power law fit, method using estimation with more geometrical parameters (Lawson & Baker, JAMC,2006)3- Examples of sampled images (capped columns) => simple geometrical model4- Work in progress:

Technical trouble:1-Small particles PSD (D<100µm). Trouble in the ROI code ?

2-Sensibility of the results to the threshold rules (example of PSD with decreasing threshold)

HAIC working document 18/11/2013 meeting

TWC hot wire (Robust)

Best A.M estimateB

Lawson & B. estimate

Ctot estimate

Three exemples of TWC estimations from imager using different rules, compared to hot wire probe.

1

2

3

1 2 3

Scatter plot (TWC from imager, TWC from hot wire probe)1- Best estimation using power law functions (see next slides)2- Lawson and Baker geometrical estimation f(A,W,L,P) (non linearity, due to Lmax estimation ?)3- Simplest estimation (using total count of hydrometeor images)

Comparisons between imager and hot wire probe

Sigma using only total concentration

Best estimation using PSD and M(D) power law Beta=1.93

B=1.93B=1.50 B=2.50

1- Look for the best linear correlation of TWC from imager and hot wire (not sensible to theA prefactor, i.e. calibration factors, DOF,….) => Estimation of B

Estimation of the mass(diameter) function mass=A*Diam^B

Step 2- Look for the best PDF correlation between the two probes Empirical estimation of the prefactor M= 3.28e-5 DsurStep 3- Verify the estimated mass to diameter law (Mass=A.DB)

assuming: shape model and hydrometeor density

- Imager… Hot wire

1.93

Capped columns Leg [12 h,12.7 h]

Best estimate of B=1.93

Irregular+agregatesLeg [12.7 h,12.8 h]

Best estimate of B=1.98(more dense objects ?)

Some examples of « well oriented » images of capped columns(more than 300 avaiable (D> 300µm) for the 10.6 Km leg [12h,12.7h]Manual estimation of 4 geometrical variables: (Lc,Wc) columns part, (Lp,Wp) plates part

1 mm

Lc

Wc

Lp

WpAssumed growth historie of these CP:1- Start as column for higth sursatuation S2- Transition to plate for low SLarge variability observed for this transition=> Dificult to define a « standard » shape

Some examples of « irregular» images (+aggregates+rosettes) for the 10.6 Km altitude sub-leg [12.7 h,12.8h] (« old » region, low concentration)

1 mm

Assumed growth historie of these cristal ?1- Nucleation of supercooled droplet2- Vapour diffusion process (low S ?)2- Agregation

On the site: CATROI_V1423allCatalogue of all ROI images of size largerthan 300µm (for identification of regions)

Tresholding sensibility analysis

Treshold definition:

Amp=(max(image)-min(image))/2Med= min(image)+AmpTresh=med-S*Amp

Binary image= image LE TreshS=0

S=0.1

S=0.2

S=0.25

GEO ROI files on the siteV1423_GEO_S000 S=0V1423_GEO_S010 S=0.1 ---------------V1423_GEO_S030 S=0.3

S=0

S=0.1

S=0.2

S=0.25

2 pix

S=0

S=0.1S=0.2

S=0.25

Tresholding sensibility analysis

Small diameter effect Large diameter effect Diameter measure shift (few pixel) Artefact for tail of large particles

Technical trouble:1-Small particles PSD (D<100µm). Trouble in the ROI code ?2-AIRBUS PSD jump ?

Diameter (µm) Diameter (µm)

• AIRBUS PSD__ ROI PSD

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