AATTMMOOSS

Evaluation of raindrop size distribution retrievals based on the Doppler spectra

using three beams

Remote-sensing of the environment (RSE)

Christine Unal

AATTMMOOSS In this talk:

� Differential reflectivity Zdr cannot be used (near-vertical profiling or light rain) Doppler spectra

� first evaluation of rain Drop Size Distribution (DSD) comparing

retrievals from the same radar resolution volume using two different

polarizations

DelftUniversity ofTechnology

2Remote-sensing of the environment (RSE)

polarizations

� second evaluation comparing DSD retrievals in different directions

during stratiform light rain

TARA radarInfluence of the radial wind on the DSD estimates

AATTMMOOSS

FM-CW Doppler-polarimetric S-band TARA radar

DelftUniversity ofTechnology

3Remote-sensing of the environment (RSE)

wind measurement available

AATTMMOOSS

Doppler spectra model

Based on

Moisseev, Chandrasekar, Unal and Russchenberg, 2006: Dual-

polarization spectral analysis for retrieval of effective raindrop shapes

Input

- Drop Size Distribution (DSD) :

� median volume diameter D0

Output

DelftUniversity ofTechnology

4Remote-sensing of the environment (RSE)

� intercept parameter Nw

� shape parameter µ

- Radial wind (v0)

- Spectral broadening (σ0)

AATTMMOOSS

Retrieval technique

Based on

Moisseev, Chandrasekar, Unal and Russchenberg, 2006: Dual-

polarization spectral analysis for retrieval of effective raindrop shapes

Non-linear optimization of D0 , µ , σ0

+ estimation of Nw , v0

DelftUniversity ofTechnology

5Remote-sensing of the environment (RSE)

+

=

AATTMMOOSS

Sensitivity analysis

Parameter D0 Nw µ σ0 v0

Region 0.2 – 3 mm 0 – 8000 mm-1 m-3 -2 – 10 0.1 – 0.9 m s-1 0 – 1.2 m s-1

RMSD 0.12 mm 1350 mm-1 m-3 0.67 0.04 m s-1 0.18 m s-1

CV(RMSD) 17% 54% 8.4% 28%

Simulation results on averaged Doppler spectra (10-55 dBZ reflectivity, average:30)

DelftUniversity ofTechnology

6Remote-sensing of the environment (RSE)

CV(RMSD) 17% 54% 8.4% 28%

Parameter Z LWC Nt

RMSD 0.30 dB 0.13 g m-3 142 m-3

CV(RMSD) 0.91% 22% 7.4%

AATTMMOOSS

Multi-beam retrieval example (non-averaged Doppler spectra)

Vertical beam75 deg 75 deg 69 deg

DelftUniversity ofTechnology

7Remote-sensing of the environment (RSE)

AATTMMOOSS

Multi-beam retrieval profile example

Vertical

Median volume diameter D0 profile in 3 different looking directions

Retrieval procedure + height-smoothing

DelftUniversity ofTechnology

8Remote-sensing of the environment (RSE)

Vertical beam

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Retrievals comparison (light rain)

HH and VV non averaged Doppler spectra

D0 Nw µ v0

DelftUniversity ofTechnology

9Remote-sensing of the environment (RSE)

σ0 Z

LWC

Nt

AATTMMOOSS

Dynamical retrieval v0

Comparison between retrieval of radial wind (-v0) and radial component of mean horizontal wind measured by TARA

DelftUniversity ofTechnology

10Remote-sensing of the environment (RSE)

AATTMMOOSS

Influence of the error on v0 on the DSD retrieval

Doppler spectra are shifted using TARA mean

DelftUniversity ofTechnology

11Remote-sensing of the environment (RSE)

Doppler spectra are shifted using TARA mean horizontal wind measurement before retrieval

AATTMMOOSS Influence of the error on v0 on the median volume diameter (D0)

Mean horizontal wind correction before retrieval1600 m

1400 m

DelftUniversity ofTechnology

12Remote-sensing of the environment (RSE)

800 m

400 m

AATTMMOOSS

Conclusions

� development of an automatic rain retrieval technique based on the complete Doppler spectrum (from drizzle to heavy rain)

� a tool for study cases

� consistency of the retrievals (D0, µ, v0, σ0, LWC) representing the same radar resolution volume

DelftUniversity ofTechnology

13Remote-sensing of the environment (RSE)

� when Zdr is too small (light precipitation, near-vertical profiling), it looks necessary to input the radial component of the mean horizontal wind to reduce the errors on (D0, Nw, µ)

� adding two other looking directions (beams) to estimate the contribution of the radial wind may solve this problem

AATTMMOOSS

Moisseev, Chandrasekar, Unal and Russchenberg, 2006: Dual-polarization spectral analysis for retrieval of effective raindrop shapes

DelftUniversity ofTechnology

14Remote-sensing of the environment (RSE)

AATTMMOOSS

Do

σ0

µ

+

+

Do min Do max

σo min σo max

µ maxµ min

L

L

L

Fitting

Do

σ0

µ

�Ite

rativ

e se

lect

ion

proc

edur

e

�O

ptim

izat

ion

pro

cedu

refr

om c

ost f

unct

ion

stud

y (L

)

� Final outcome( ) ( )

max

min

2mod0 , , 0( ) ,

vmeasHH dB HH dB

v v

L D sZ v sZ v D=

= − ∑

( )max

0min

2mod

0 , , 0( ) ( , )v

measHH dB HH dB D

v v

L sZ v sZ vσ σ=

= − ∑

DelftUniversity ofTechnology

15Remote-sensing of the environment (RSE)

Nw

V0

+

measured spectrum

Fitting

Model spectrum

�es

timat

ion

proc

edur

e

� Output of the model

Max cross-correlation

Variable projection method (Rust, 2003)

Error calculationNon linear least-square algorithm

Estimation from Do, σ0 and µ

+ ( ) ( )max

0 0min

2mod

, , ,( ) ,

vmeasHH dB HH dB D

v v

L sZ v sZ vσ

µ µ=

= − ∑

AATTMMOOSS

Retrieval outputs: dsd, v0 and σσσσ0

� median volume diameter D0

� µ bounded [-1 5]

� spectral broadening σ0 bounded [0 1] m s-1

�intercept parameter Nw free

� radial wind v0 free

DelftUniversity ofTechnology

16Remote-sensing of the environment (RSE)

crucial to get the actual D0

help

Polarimetry with Zdr

Wind estimates with v0

Reflectivity

AATTMMOOSS

Multi-beam raindrop size comparison (Z-D0)

Mean horizontal wind correction before retrieval1600 m

1400 m

DelftUniversity ofTechnology

17Remote-sensing of the environment (RSE)

800 m

400 m

AATTMMOOSS

Multi-beam raindrop size comparison (Nw)

Mean horizontal wind correction before retrieval1600 m

1400 m

DelftUniversity ofTechnology

18Remote-sensing of the environment (RSE)

800 m

400 m

AATTMMOOSS

Multi-beam raindrop size comparison (µµµµ)

Mean horizontal wind correction before retrieval

1600 m

1400 m

DelftUniversity ofTechnology

19Remote-sensing of the environment (RSE)

800 m

400 m