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WP2: Extraction of Information from Doppler Winds. G. Haase, T. Landelius and D.M. Michelson. Swedish Meteorological and Hydrological Institute. Doppler wind measurements. Quality control (e.g. de-aliasing). Assimilation into NWP models (e.g. VAD profiles, superobservations …). - PowerPoint PPT Presentation
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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
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