G. Haase, T. Landelius and D.M. Michelson

WP2: Extraction of Information from Doppler Winds

Swedish Meteorological and Hydrological Institute

Doppler wind measurements

Assimilation into NWP models

(e.g. VAD profiles, superobservations …)

Quality control

(e.g. de-aliasing)

Aliasing problem

4

PRF

nv

8

cvr nn

Doppler

“dilemma”

De-aliasing algorithm

Linear wind model: coscoscossin - vu

De-aliasing algorithm

Linear wind model: coscoscossin - vu

Map the measurements onto the surface of a torus

Case study

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

Case study

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

observed velocity de-aliased velocity

Validation

VO not aliased

(VO ≤ VN)

VO aliased

(VO > VN)

VO correctly

de-aliased

39.3 %

(36.4 %)

60.6 %

(59.1 %)

VO falsely

de-aliased

0.0 %

(3.0 %)

0.1 %

(1.5 %)

Sample size: 388,147 pixelsNyquist velocity: 7.55 m/s

Hemse (Sweden): 2 July 2003, 10:47 UTC

Application 1: Wind profiles (VVP)

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

Application 2: Superobservations

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

Application 2: Superobservations

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

observed velocity de-aliased velocity

Summary

• accurate & robust post-processing algorithm

(elimination of multiple folding)

• no additional wind information needed

(independent data source)

• improved quality of wind profiles and superobservations for data assimilation

To do

• validate the new de-aliasing algorithm for convective precipitation events

• generate de-aliased superobservations:

SMHI & FMI: July 2000 + January 2002

• prepare real-time application

Deliverables

• Report: Radar radial wind superobservations

(http://carpediem.ub.es)

• Data sets