Improving radar Doppler wind information extraction

Yong Kheng Goh, Anthony HoltUniversity of Essex, U. K.

Günther Haase, Tomas Landelius SMHI, Sweden

Radar Observations

● Some radars used in operational forecasting only provide Reflectivity data.

● Some others provide Radial Velocity data from Doppler measurements. They can suffer from velocity ambiguity due to folding.

● In this study we also make use of the reasonably close proximity of two Doppler radars in the Po Valley in Italy.

CARPE DIEM WP2 objectives

● Improve the use of Doppler wind data via:

1) Super-observation product (SMHI)

2) Operational dual-Doppler wind retrieval

(ESSEX)

Dual Doppler Wind Retrieval

Procedure:1) Terrain analysis – establish areas

amenable to dual-Doppler analysis.2) Data gridding – interpolating polar

data into Cartesian data.3) Calculating wind field.4) Verifying wind field (a) by re-constructing PPI and

comparing with original PPI; (b) comparing “along-track” components.

Italian Po Valley

Problems with terrain and radar orientation

Height Profile

Italian terrain

Location of Po Valley study region

Data Gridding● Typical dimension:

– 60 x 60 cells x 4 layers– 0.5 km x 0.5 km x 0.9

km● Search and average

method.● Resource hungry

process.● E.g. 60x60x50x50x4

= 36,000,000 times per data set.

Example of polar to Cartesian conversion

● Data type : Doppler velocity● 60 x 60 grid, lattice length = 0.5 km.

Calculating wind field

● Fundamental equations:

iiti

r

iiiiii

Vwv

wvu

sin

sincoscoscossinˆ)(

vr

0

vvv

tt

velocityterminal velocityradialnet

radar at t measuremenlocity Doppler ve

vector(velocity) wind),,()(

ti

ir

wV

iv

wvuv

Numerical procedures● Iterative method:

– horizontal components

– vertical component

● Boundary conditions: – zero velocity on ground

● Typical convergent factor

11, nnnn wDCvwBAu

dz

dw

z

w

y

v

x

u 1

where0

030.0,,

)1(,,

)(

,,)(

,,)1(

max

kjin

kjin

kjin

kjin

ww

ww

Comparison of reconstructed radial velocity field and radar measurement

measurement Reconstructed

“along-track” componentsv

r1

r2

r12

v . (r12 + r2 – r1) = 0

= v(cal).r12 + vr2 (obs) r2 – vr1 (obs) r1

Typical relative deviation, /(v .r12) < ±1%

Assimilation into NWP models

Quality control(e.g. de-aliasing)

WP2: de-aliasing & super-observations (SMHI)

Radarwinds

VVP profiles andsuper-observations

• Innovation: radar observations are mapped onto the surface of a torus (assuming linear winds)

• Advantage: no need for additional wind data from other instruments or NWP models

• Performance: accurate and robust tool for eliminating multiple folding

• Assimilation: benefits through improved quality of wind profiles and super-observations

De-aliasing algorithm

Vantaa (Finland): 4 December 1999, 12:00 UTC

Wind velocity profiles (VVP)

Wind direction profiles (VVP)

Vantaa (Finland): 4 December 1999, 12:00 UTC

Super-observations

Vantaa (Finland): 4 December 1999, 12:00 UTC

tow

ard

s

a

wa

y

De-aliased Doppler measurements

Gattatico (Italy): 31 July 2003, 17:34 UTC

tow

ard

s

a

wa

y

Summary

● Real time dual Doppler wind retrieval can provide useful 3D wind velocity field information to the weather radar operators.

● De-aliased Doppler winds can be assimilated into NWP models through super-observations.

● To-do:– Comparison with NWP model.– Triple Doppler wind retrieval.