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

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 …).

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

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  1. WP2: Extraction of Information from Doppler Winds G. Haase, T. Landelius and D.M. Michelson Swedish Meteorological and Hydrological Institute

  2. Doppler wind measurements Quality control (e.g. de-aliasing) Assimilation into NWP models (e.g. VAD profiles, superobservations …)

  3. Aliasing problem Doppler “dilemma”

  4. De-aliasing algorithm Linear wind model:

  5. De-aliasing algorithm Linear wind model: Map the measurements onto the surface of a torus

  6. Case study Vantaa (Finland): 4 December 1999, 12:00 UTC

  7. Case study Vantaa (Finland): 4 December 1999, 12:00 UTC observed velocity de-aliased velocity

  8. Validation Hemse (Sweden): 2 July 2003, 10:47 UTC Sample size: 388,147 pixels Nyquist velocity: 7.55 m/s

  9. Application 1: Wind profiles (VVP) Vantaa (Finland): 4 December 1999, 12:00 UTC

  10. Application 2: Superobservations Vantaa (Finland): 4 December 1999, 12:00 UTC

  11. Application 2: Superobservations Vantaa (Finland): 4 December 1999, 12:00 UTC observed velocity de-aliased velocity

  12. 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

  13. 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

  14. Deliverables • Report: Radar radial wind superobservations • (http://carpediem.ub.es) • Data sets

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