1. Performance Characteristics of a Pseudo-operational Ensemble Kalman Filter April 2006, EnKF Wildflower Meeting Greg Hakim & Ryan Torn University of Washington http://www.atmos.washington.edu/~hakim

2. Outline Issues for limited-area EnKFs. –Boundary conditions. –Nesting. –[Multiscale prior covariance.] UW pseudo-operational system. –Performance characteristics. –Analysis of Record (AOR) test. Experiments using the UW RT data. –Sensitivity & targeting. –Observation impact & thinning.

3. Boundary Conditions Obvious choice: global ensemble, but… –Often ensembles too small. –Undesirable ensemble population techniques. –Different resolution, grids, etc. Flexible alternatives (Torn et al. 2006). –Mean + random draws from N(0,B). –Mean + scaled random draws from climatology. –“error boundary layer” shallow due to obs.

4. Nesting Grid 1: global ensemble BCs. –E.g. draws from N(0,B) or similar. Grid 2: ensemble BCs from grid 1. One-way nesting: straightforward. –Cycle on grid 1, then on grid 2. Two-way: many choices; little experience. –Note: Hx b different on grids 1 and 2. –Issues at grid boundaries. 1 2

5. The Multiscale Problem Sampling error –noise in obs est & prior covariance. Ad hoc remedies –“localization” –Confidence intervals. Multiscale problem. –Noise on smallest scales may dominate. –Need for scale-selective update?

6. Surface Temperature Covariance

7. Mesoscale Example: cov(|V|, q rain )

8. Real Time Data Assimilation at the University of Washington

9. Objectives of System Evaluate EnKF in a region of sparse in-situ observations and complex topography. Estimate analysis & forecast error. Sensitivity: targeting & thinning.

10. Model Specifics WRF Model, 45 km resolution, 33 vertical levels 90 ensemble members 6 hour analysis cycle ensemble forecasts to t+24 hrs at 00 and 12 UTC perturbed boundaries using fixed covariance perturbations from WRF 3D-VAR

11. Observations Obs. TypeVariables00 UTC06 UTC12 UTC18 UTC SurfaceAltimeter430420 440 Rawindsondeu, v, T, RH10000 0 ACARSu, v, T165013907401860 Cloud Windu, v2030174016701510 Total5110355038303810

12. Probabilistic Analyses Large uncertainty associated with shortwave approaching in NW flow sea-level pressure500 hPa height

13. Microphysical Analyses model analysiscomposite radar 20 February 2005, 00 UTC

14. Ensemble Forecasts Analysis24-hour forecast

15. Verification

16. Temperature Verification 12 hour forecast24 hour forecast UW EnKF GFS CMC UKMO NOGAPS ECMWF

17. U-Wind Verification 12 hour forecast24 hour forecast UW EnKF GFS CMC UKMO NOGAPS ECMWF

18. Moisture Verification (T d ) 12 hour forecast24 hour forecast UW EnKF GFS CMC UKMO NOGAPS ECMWF

19. No Assimilation Verification UW EnKF No Observations Assimilated WindsTemperature

20. Moving Toward the Mesoscale

21. Analysis of Record Hourly surface analyses. EnKF covariances. Available t+30 minutes. 15 km resolution.

22. Hurricane Katrina at 10 km

23. Sensitivity Analysis Basic premise: –how do forecasts respond to changes in initial & boundary conditions, & the model? Applications: –“targeted observations” & network design. –“targeted state estimation” (thinning). –basic dynamics research.

24. Adjoint approach Given J, a scalar forecast metric, one can show that: Need to run an adjoint model backward in time. Complex code & lots of approximations Does not account for state estimation or errors. adjoint of resolvant

25. Ensemble Approach Adjoint sensitivity weighted by initial-time error covariance. Can evaluate rapidly without an adjoint model! Can show: this gives response in J, including state estimation. With Brian Ancell (UW)

26. Sensitivity from the UW Real-time system Case study removing one observation. Metric: average MSL pressure over western WA

27. Sensitivity Demonstration How would a forecast change if buoy 46036 were removed?

28. Overview of Case

33. Sea-level pressure850 hPa temperature 12 UTC 5 Feb Sensitivity

34. Analysis ChangeForecast Sensitivity 12 UTC 5 Feb. Analysis Change

35. Forecast Differences Assimilating the surface pressure observation at buoy 46036 leads to a stronger cyclone. Predicted Response: 0.63 hPa Actual Response: 0.60 hPa

36. Summary of 10 Cases

37. Observation Impact Adaptively sampling the obs datastream –Thin by assimilating only high-impact obs.

38. Observations Ranked by Impact

39. Ob-Type Contributions to Metric

40. Metric Prediction Verification

41. Summary BCs: flexibility & weak influence. UW real-time system ~gov. center quality. –Moisture field better than most. –Surface AOR ~10 km. Sensitivity analysis. –Ensemble targeting easy & flexible. –Adaptive DA (“thinning”).

43. AOR Opportunities “No propagate” update – nested high resolution single member. – assimilate using coarse-grid stats. – can be done “now.” Deterministic propagation – as above, but evolve high-res state. Full filter – evolve & assimilate entire ensemble. 4DVAR with EnKF statistics. – at least 3--5 years out.

44. AOR Challenges True multiscale conditions (<15 km). –Scale-dependent sampling errors? Bias estimation and removal. –EnKF allows state-dependent bias estimation. Model error estimation & removal. –Parameter estimation; model calibration. Satellite radiance assimilation. Kalman smoothing.

45. IR Temperature Analyses model analysisIR satellite image 30 March 2005, 12 UTC

46. Global Perturbations Create a number of draws from N(0,B) + Add to deterministic boundary condition and calculate tendency Randomly choose Ne draws and scale to desired variance.

47. Height Verification 12 hour forecast24 hour forecast UW EnKF GFS CMC UKMO NOGAPS ECMWF

48. Surface Obs. and Rawindsondes

49. Observation Densities aircraft obs.cloud winds

50. Ensemble inliers/outliers inlieroutlier