Design and Validation of the GMAO OSSE Ronald M. Errico Nikki Prive’ King-Sheng Tai

1. Design and Validation of the GMAO OSSE Ronald M. Errico Nikki Prive’ King-Sheng Tai Progress report presented to the GMAO 23 Feb. 2012

2. Acknowledgements Runhua Yang Meta Sienkiewicz Jing Guo Ricardo Todling Will McCarty Arlindoda Silva Joanna Joiner Joe Stassi Ops Group et al.

3. Outline: • Methodology • Assimilation metrics • Forecast metrics • Application: Characterization of analysis error • Application: Consideration of future instruments • Summary

4. Data Assimilation of Real Data Real Evolving Atmosphere, with imperfect observations. Truth unknown Analysis Analysis Analysis Time Analysis Analysis Analysis Climate simulation, with simulated imperfect “observations.” Truth known. Observing System Simulation Experiment

5. Applications of OSSEs • Estimate effects of proposed instruments (and their competing designs)on analysis skill by exploiting simulated environment. • 2. Evaluate present and proposed techniques for data assimilation by exploiting known truth.

6. Validation of OSSEs Compare a variety of statistics that can be computed in both real assimilation and OSSE contexts. These statistics vary in their importance and in their ability to be tuned in the OSSE context.

7. Methodology

8. ECMWF Nature Run • Free-running “forecast” • Operational model from 2006 • T511L91 reduced linear Gaussian grid (approx 35km) • 10 May 2005 through 31 May 2006 • 3 hourly output • SST and sea ice cover is real analysis for that period

9. Assimilation System GEOS-5.7.1p2 (GSI 3DVAR) Resolution 0.5x0.625 degree grid, 72 levels (eta coordinate) Evaluation for 1-31 July 2005 Spin-up starts15 June 2005 from a previous 2-day accelerated spin up Conventionalobservations include: prepbufr without GOES radiances or precipitation observations Radiance observation include: HIRS-2 (N14), HIRS-3 (N16,17), AMSU-A (N15,16,AQUA; without channel 1-3,15), AMSU-B (N15,16,17),AIRS (AQUA), MSU (N14) Radiance bias correction initialized with OSSE spun up file

10. Simulation of Observations • 1. All observations created using bilinear interpolation horizontally, • log-linear interpolation vertically, linear interpolation in time • 2. Radiance observations created using CRTM version 1.2 • 3. Clouds and precipitation are treated as elevated black bodies • 4. No use of NR snow coverage • 5. Locations for all “conventional” observations given by • corresponding real ones, except no drift for RAOBS • 6. SATWNDS not associated with trackable features in NR

12. Dependency of results on errors Consider Kalman filter applied to linear model Daley and Menard 1993 MWR

13. Simulation of Explicit Observation Errors • Some representativeness error implicitly present • Gaussian noise added to all observations • AIRS errors correlated between channels • 4. Observational errors for SATWND and non-AIRS radiances • horizontally correlated (using isotropic, Gaussian shapes) • 5. Conventional soundings and SATWND observational errors • vertically correlated (Gaussian shaped in log-p coordinate) • 6. Tuning parameters are error standard deviations, fractions of • variances for correlated components, vertical and horizontal • length scales

14. AMSU-A NOAA-15 RAOB Standard deviations of Observation errors In GSI error tables (solid circles or lines) vs. For adding obs. errors (open circles or dashed lines) RAOB GOES-IR Winds

15. Assimilation Metrics

16. Locations of QC-accepted observations for AIRS channel 295 at 18 UTC 12 July Simulated Real

17. Standard deviations of QC-accepted O-F values (Real vs. OSSE) AIR AQUA AMSU-A NOAA-15 RAOB U Wind RAOB q

19. Horizontal correlations of O-F AMSU-A NOAA-15 Chan 6 GLOB RAOB T 700 hPa NHET GOES-IR SATWND 300 hPa NHET GOES-IR SATWND 850 hPa NHET

20. Square roots of zonal means of temporal variances of analysis increments T OSSE T Real U Real U OSSE

21. OSSE Real T 850 hPa Time mean Analysis increments U 500 hPa

22. Forecast Metrics

23. OSSE vs Real Data: Forecasts Mean anomaly correlations Real Control OSSE Control

25. January Forecasts Real Control OSSE Control

26. U-Wind RMS error: July Solid lines: 24 hour RMS error vs analysis Dashed lines: 120 hr forecast RMS error vs analysis Real Control OSSE Control

27. T RMS error: July Solid lines: 24 hour RMS error vs analysis Dashed lines: 120 hr forecast RMS error vs analysis Real Control OSSE Control

28. Real OSSE

29. Real OSSE

30. Real OSSE

31. Impact of obs errors on forecast

32. July Adjoint: dry error energy norm

33. July Adjoint: dry error energy norm

34. Analysis and Forecast Error Vs. Nature Run

35. RMS OSSE Error vs NR, Temperature

36. RMS Error vs NR, U wind

37. 400 hPa analysis error, U (m/s) July mean error vs NR

38. A-B at 400 hPa

39. Application: Characterization of analysis error (as an example of the kinds of calculations that can be performed)

40. Square roots of zonal means of temporal variances of U wind analysis error

41. Fractional reduction of zonal means of temporal variances of analysis errors compared with background errors T u

42. Horizontal spectra of analysis and analysis error (Spectra of time-mean fields subtracted) Rotational Wind 200 hPa Divergent Wind 200 hPa KE (J/kg) KE (J/kg) wave number n wave number n

43. Horizontal correlation length scales for v wind (Explicit bkg error vs. NMC method estimate) South Pacific Tropical Pacific

44. Application: Consideration of future instruments

45. ESA Aeolus • Utilize OSSE framework to generate realistic Aeolus proxy data • Lidar Performance Analysis Simulator (LIPAS), via KNMI (G.-J. Marseille & A. Stoffelen) • Need proper sampling (spatial & vertical), yield, and errorcharacteristics • Active measurements of wind from space • Measuring the Doppler shift imposed on lidar backscatter • Launch delayed till late 2013 • 408 km, dawn-dusk, sun-synchronous orbit • Wind measurements 90° off-track, 35° off-nadir • Nearly u @ equator • No spaceborne heritage for DA development

46. Assimilation Results Large reductions of RMS seen over tropics • Plots show reduction in analyzed u-wind RMS (compared to the NR as truth) by adding Aeolus observations to GMAO OSSE • Negative values indicate improvement • Existing Observations primarily of mass field (passive satellite radiances) • Winds cannot be inferred through balance assumptions Zonal Wind RMS Difference (ms-1)

47. Assimilation Results Tropics (-30º to 30º) NH Extratropics (30º to 90º) SH Extratropics (-90º to -30º) • Impact at 200 hPa consistent through troposphere • Less impact towards surface • Less observations • Increased contamination • Lesser impacts in N./S. hemispheres • Winds through mass-wind balance • Less impact in mass fields, but still generally positive (not shown)

48. Conclusions, Caveats, and Future Efforts • These results are overstated! • Error in simulated DWL observations inherently understated • No added representativeness error • Error in Yield • Lack of clouds in NR not accounted for in DWL obs simulation • Shows expected result that largest impact is expected in tropics • Future work • Include incorporation of L2B processing into DA System (GSI) • Software release delayed due to change in instrument operations • A necessary level of processing for use of data in near-realtime • Will be included as part of outer loop processing • Redo experiment w/ simulator updated for change in instrument ops • Simulator update in progress @ KNMI

49. Summary

50. Summary • 1. Fairly easy to match O-F covariances • 2. Harder to match analysis increment statistics • 3. Hardest to match forecast error metrics • 4. Present OSSE framework validates reasonably well, but … • 5. Some correctable deficiencies • 6. Some puzzles