Slide 1 Wind Lidar working group February 2010 Slide 1 Spaceborne Doppler Wind Lidars - Scientific motivation and impact studies for ADM/Aeolus Erland.

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Presentation transcript:

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

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

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

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

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

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

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

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

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

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

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

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

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

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 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) →

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

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

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

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

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

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

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

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: (2007)

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

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

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

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

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