[email protected], slide 1 German Aerospace CenterMicrowaves and Radar Institute Methodology for obtaining physical parameters from fully polarimetric.

[email protected], slide 1German Aerospace Center Microwaves and Radar Institute

Methodology for obtaining physical parameters from fully polarimetric

coherent weather radar data: A first approach to unsupervised

Entropy-Alpha-classification

Dr. Thomas BörnerDLR Oberpfaffenhofen

Microwaves and Radar InstituteD-82234 Weßling

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Outline

• Introduction to the H- decomposition theorem• Classification scheme• Analysis and interpretation of the polarimetric time

series data set• Conclusions• Future activities

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Decomposing the Scattering Matrix

yyyx

xyxxL SS

SSS TyyxyxxL SSSs 23

0

0

01

10

10

01

10

01

i

idcba

baidc

idcbaS

TxyyyxxyyxxLP SSSSSss 22

1

020

101

101

2

133

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Entropy H and angle

3

1

*

*

3

2

1

3

2

1

321*33

00

00

00

|||

|||

i

Tiii

T

TPP ww

w

w

w

wwwssC

.wherelog3

1

3

13

jj

ii

iii PPPH

3

1iiiP

sinsin

cossin

cos

with33i

iPP

e

ewwss

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What does tell us?

= 0 = 90 = 45

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H- feasible area

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“Classic” Products

Zyy [dBZ] ZDR [dB]

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Extracted Products

Entropy H angle [deg]

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Populated H- plane

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Zyy and Classification

Zyy [dBZ] Classes

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15 consecutive PPI Sector Scans

Zyy [dBZ] Classes

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Is there a Z-H relation?

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Is there a Z- relation?

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Conclusions

• It has been shown that it is possible to apply the H- decomposition to polarimetric weather radar data and to retrieve meaningful results.

• The classification provides knowledge about different types of scatterers without having to access empirical a-priori knowledge.

• Proper interpretation of PPI scans is difficult, because it is unclear what the radar beam is actually scanning.

• Regions with ground clutter can be easily detected, which might help to enhance clutter filtering.

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Future Activities

• Analyse RHI scans: different layers are easier to distinguish enhance the classification scheme!

• Compare results with other sources of information about particles, preferably simultaneously collected.

• Compare results with other classification methods.

• Use additional parameters as 1 or the anisotropy to refine the classification scheme.