1. Slide 1 Wind Lidar working group February 2010 Slide 1 Spaceborne Doppler Wind Lidars - Scientific motivation and impact studies for ADM/Aeolus Erland K ällén with help from David Tan, Carla Cardinali, Paul Berrisford ECMWF

2. Slide 2 Wind Lidar working group February 2010 Slide 2 Outline  ADM/Aeolus  Scientific motivation  Present observing system  Forecast error Sensitivity to Observations  Re-analysis uncertainties  ADM/Aeolus impact study  Conclusions

3. Slide 3 Wind Lidar working group February 2010 Slide 3 Atmospheric Dynamics Mission ADM/Aeolus

4. Slide 4 Wind Lidar working group February 2010 Slide 4 [H]LOS ADM-Aeolus Doppler Lidar Aerosol and molecular scattering Intermittent pulses Only one wind component Dawn-dusk polar orbit Measurement error < 2 m/s

5. Slide 5 Wind Lidar working group February 2010 Slide 5 ADM/Aeolus

6. Slide 6 Wind Lidar working group February 2010 Slide 6 Main scientific objectives of ADM/Aeolus  Improve representation of wind field in atmospheric analyses  Tropics: Wind field governs dynamics  Mid-latitudes: Intense storm developments and meso-scale circulation systems  Numerical weather prediction  Climate sensitivity

7. Slide 7 Wind Lidar working group February 2010 Slide 7 Additional objectives  Aerosol information  Cloud properties

8. Slide 8 Wind Lidar working group February 2010 Slide 8 Outline  ADM/Aeolus  Scientific motivation  Present observing system  Forecast error Sensitivity to Observations  Re-analysis uncertainties  ADM/Aeolus impact study  Conclusions

9. Slide 9 Wind Lidar working group February 2010 Slide 9 Present observing system  Radiosondes  Pilot balloons and profilers  Buoys  Satellites  Aircraft data

10. Slide 10 Wind Lidar working group February 2010 Slide 10 Radiosondes 1 Nov 2004, ECMWF Total: 590

11. Slide 11 Wind Lidar working group February 2010 Slide 11 Satellite polar orbiting 1 Nov 2004, ECMWF Total: 247309

12. Slide 12 Wind Lidar working group February 2010 Slide 12 Aircraft data 1 Nov 2004, ECMWF Total 26219

13. Slide 13 Wind Lidar working group February 2010 Slide 13 Outline  ADM/Aeolus  Scientific motivation  Present observing system  Forecast error Sensitivity to Observations  Re-analysis uncertainties  ADM/Aeolus impact study  Conclusions

14. Slide 14 Wind Lidar working group February 2010 Slide 14 Forecast error Sensitivity to Observations Analysis solution: Forecast error sensitivity to the analysis x a : Rabier F, et al. 1996. Compute the δJ: Forecast error J (“dry energy norm” p s, T, u, v) The tool provides the Forecast Error Contribution for each assimilated observation, which can be accumulated by observation type, subtype, variable or level → (y: observations) →

15. Slide 15 Wind Lidar working group February 2010 Slide 15 24 H Forecast Error Contribution of GOS

16. Slide 16 Wind Lidar working group February 2010 Slide 16 Mass versus Wind contributions

17. Slide 17 Wind Lidar working group February 2010 Slide 17 Outline  ADM/Aeolus  Scientific motivation  Present observing system  Forecast error Sensitivity to Observations  Re-analysis uncertainties  ADM/Aeolus impact study  Conclusions

18. Slide 18 Wind Lidar working group February 2010 Slide 18 Re-analyses of zonal winds Kistler et al., 2001 NCEP ERA-15 Difference NCEP/ERA-15

19. Slide 19 Wind Lidar working group February 2010 Slide 19 ERA-Interim Zonal mean wind 1989-2001 m/s >15 >30 30 >25 <-10

20. Slide 20 Wind Lidar working group February 2010 Slide 20 Difference ERA-Interim vs. ERA-40 Zonal mean wind 1989-2001 m/s >2 <-4

21. Slide 21 Wind Lidar working group February 2010 Slide 21 Outline  ADM/Aeolus  Scientific motivation  Present observing system  Forecast error Sensitivity to Observations  Re-analysis uncertainties  ADM/Aeolus impact study  Conclusions

22. Slide 22 Wind Lidar working group February 2010 Slide 22 Assimilation study for ADM/Aeolus  Assimilation ensembles for data impact assessment  Use ensemble spread as proxy for short-range forecast errors (background errors)  By extension, good data reduce ensemble spread  DWL impact  Radiosonde/profiler impact - provides calibration  Tan et al., QJRMS 133:381-390 (2007)

23. Slide 23 Wind Lidar working group February 2010 Slide 23 Reference Result VerificationNWP-SystemObservations Reference Result An & Fc Diagnostics NWP-System Ensemble Observations OSE Assimilation Ensemble Real atmosphere Assimilation/ forecast Compare to reference Impact assessment Ref. run Assimilation/ forecast Ensemble spread Assimilation/ forecast Ensemble spread Calibrate Impact assessment

24. Slide 24 Wind Lidar working group February 2010 Slide 24 Data impact on ensemble forecasts - zonal wind spread at 500 hPa SondesControl ADM-Aeolus  Radiosondes and wind profilers over Japan, Australia, N.Amer, Europe  DWL over oceans & tropics

25. Slide 25 Wind Lidar working group February 2010 Slide 25 Data impact on ensemble forecasts - zonal wind spread at 200 hPa Sondes Control ADM-Aeolus  Radiosondes and wind profilers over Japan, Australia, N.Amer, Europe  DWL over oceans and tropics

26. Slide 26 Wind Lidar working group February 2010 Slide 26 Conclusions  Wind data is lacking in present global observing system  Tropical analyses suffer  Climate system re-analyses uncertain in tropics, polar areas and stratosphere  ADM/Aeolus will provide vertical wind profiles with global coverage

27. Slide 27 Wind Lidar working group February 2010 Slide 27 Thank you for your attention– questions?