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Improving radar Doppler wind information extraction

Yong Kheng Goh, Anthony Holt University of Essex, U. K. Günther Haase, Tomas Landelius SMHI, Sweden. Improving radar Doppler wind information extraction. Radar Observations. Some radars used in operational forecasting only provide Reflectivity data.

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Improving radar Doppler wind information extraction

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  1. Yong Kheng Goh, Anthony Holt University of Essex, U. K. Günther Haase, Tomas Landelius SMHI, Sweden Improving radar Doppler wind information extraction

  2. 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.

  3. CARPE DIEM WP2 objectives • Improve the use of Doppler wind data via: 1) Super-observation product (SMHI) 2) Operational dual-Doppler wind retrieval (ESSEX)

  4. 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.

  5. Italian Po Valley

  6. Problems with terrain and radar orientation

  7. Height Profile

  8. Italian terrain

  9. Location of Po Valley study region

  10. 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.

  11. Example of polar to Cartesian conversion • Data type : Doppler velocity • 60 x 60 grid, lattice length = 0.5 km.

  12. Calculating wind field • Fundamental equations:

  13. Numerical procedures • Iterative method: • horizontal components • vertical component • Boundary conditions: • zero velocity on ground • Typical convergent factor

  14. Comparison of reconstructed radial velocity field and radar measurement measurement Reconstructed

  15. “along-track” components v r1 r2 r12 v . (r12 + r2 – r1) = 0  = v(cal).r12+ vr2(obs) r2 – vr1(obs) r1 Typical relative deviation, /(v .r12) < ±1%

  16. WP2: de-aliasing & super-observations (SMHI) Quality control (e.g. de-aliasing) Radar winds VVP profilesand super-observations Assimilation into NWP models

  17. De-aliasing algorithm • 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

  18. Wind velocity profiles (VVP) Vantaa (Finland): 4 December 1999, 12:00 UTC

  19. Wind direction profiles (VVP) Vantaa (Finland): 4 December 1999, 12:00 UTC

  20. Super-observations towards away Vantaa (Finland): 4 December 1999, 12:00 UTC

  21. De-aliased Doppler measurements towards away Gattatico (Italy): 31 July 2003, 17:34 UTC

  22. 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.

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