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

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

15. Comparison of reconstructed radial velocity field and radar measurement measurementReconstructed

16. “along-track” components v r1r1 r2r2 r 12 v. (r 12 + r 2 – r 1 ) = 0  = v (cal).r 12 + v r2 (obs) r 2 – v r1 (obs) r 1 Typical relative deviation,  /(v.r 12 ) < ±1%

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

19. 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

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

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

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

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

24. 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.