Met Office Ensemble System Current Status and Plans

1. Met Office Ensemble System Current Status and Plans Neill Bowler Neill.Bowler@metoffice.gov.uk

2. Outline • Current status and plans • Initial conditions perturbations • Model physics perturbations • Latest results

3. MOGREPS LAMEPS

4. Global Ensemble Prediction Developments • Ensemble under development for short-range • ETKF perturbations • Stochastic physics • T+72 global, N144 resolution (~90km in mid-latitudes), 38 levels • Run at 0Z & 12Z

5. LAM Ensemble Prediction Developments • Ensemble under development for short-range • Regional ensemble over N. Atlantic and Europe (NAE) • Nested within global ensemble for LBCs • IC perturbations taken directly from global model • Stochastic physics • T+36 regional, 24km resolution, 38 levels • Run at 6Z & 18Z NAE

6. Time-line for technical developments ETKF to generate NAE IC perturbations Global ensemble run operationally NAE ensemble run operationally Development begins Ensemble products available in real time? Summer ‘03 10 June ‘05 2 August ‘05 Spring ‘06 Summer ‘06 We are here!

7. Initial conditions perturbations

8. EnKF • Ensemble Kalman filter is a data assimilation scheme which solves • The analysis error covariance matrix is updated according to

9. ETKF • The ETKF uses the fact that the analysis error covariance for the EnKF can be written as • So, the updated perturbations are given by • Thus, for the ETKF, the set of analysis perturbations are a linear combination of the forecast perturbations

10. Initial conditions perturbations • Perturbations centred around 4D-Var analysis • Transforms calculated using same set of observations as used in 4D-Var (including all satellite obs) within +/- 3 hours of data time • Ensemble uses 12 hour cycle (data assimilation uses 6 hour cycle)

11. Model physics perturbations

12. Model error: parameterisations • QUMP (Murphy et al., 2004) • Initial stoch. Phys. Scheme for the UM (Arribas, 2004) Random parameters

13. Short-range impacts Intense snowfall over the UK (poorly forecast)

14. SKEB Stochastic Kinetic Energy Backscatter (SKEB) Based on original idea and previous work by Shutts (2004) Aim: To backscatter (stochastically) into the forecast model some of the energy excessively dissipated by it at scales near the truncation limit In the case of the UM, a total dissipation of 0.75 Wm-2 has been estimated from the Semi-lagrangian and Horizontal diffusion schemes. (Dissipation from Physics to be added later on) Each member of the ensemble is perturbed by a different realization of this backscatter forcing

15. SKEB Streamfunction forcing: K.- Kinetic En.; R.- Random field; D.- Dissipated en. in a time-step R is designed to reproduce some statistical properties found with CRMs Example: u increments at H500 • Largest at the jets/storm track

16. SKEB • Preliminary results: • Positive increase in spread (comparable to that seen at ECMWF) Increase in spread respect to an IC-only ensemble 500 hPa geopotential height SKEB RP+SCV

17. Latest results • Have run a global ensemble forecast with 16 members + control for 2 case studies • Overall the results are promising • There was a bug in the code for the first case study which may have a minor effect on the results

18. 500hPa Height Power Spectra • The perturbations have similar spectra to full forecast fields (as one might expect). Avoid perturbing largest scales Full Forecast Field Need greater influence from stochastic physics to generate small-scale perturbations Perturbation

19. Case study 1 – Spanish plume

20. Mean and Spread PMSL

21. Spread of the Ensemble with Latitude RMS innovations for sonde observations of T Perturbation spread (at observation locations)

22. The Effect Of Stochastic Physics With stochastic physics Without stochastic physics

23. Case study 2 – 8 January 2005 Contours – PMSL Colours – θw on 850hPa

24. Postage Stamps - PMSL

25. Spread & Error NH extra-tropics Error in ensemble mean (wrt radiosonde observations) Spread Error in ensemble mean, with correction for obs errors

26. Rank Histogram Saetra et al., MWR, 132 (6) P1487 (2004)

27. Rank Histogram

28. Local EnKF LEKF: For each grid-point, draw a box around the point, and update ensemble at that point using information in the box only. I. Szunyogh (with permission)

29. Local ETKF • Calculate transform matrix using observations local to a limited set of points, approximately evenly distributed around globe • Interpolate transform matrix to intermediate grid points

30. Spherical simplex ETKF Traditional ETKF Spherical simplex (analysis perturbations are all orthogonal) (Wang, Bishop & Julier, 2004) • Using the spherical simplex transform constrains the transform matrix to have a certain form, helping to ensure consistency of perturbations

31. Local ETKF Spread Error in ensemble mean (wrt radiosonde observations) Spread Error in ensemble mean, with correction for obs errors

32. Conclusions • ETKF performance is promising • Positives are: • Near-flat rank histograms • Seems to capture the major errors in the forecast • Issues are: • Spread in tropics • Speed of growth of spread

33. Future work • Technical work in preparation for implementation • Develop stochastic physics scheme (SKEB) • Work on local ETKF scheme, and problems with tropics • Longer runs for objective comparisons with other schemes (e.g. error breeding) • Investigate usefulness of singular vectors

34. Innovation distrubtion – Sonde Temp Innovations Perturbations

35. Innovation distrubtion – ATOVS channel 26 Innovations Perturbations

36. Innovation distrubtion – ATOVS channel 40 Innovations Perturbations

37. Innovation distrubtion – Aircraft Temp Innovations Perturbations

38. Model error: excessive diffusion New approaches • Stochastic Backscatter (Shutts, 2004) • Hypothesis: model KE dissipation rate is too large With Stoch. Back. No Stoch. Back. Missing energy!

39. Inflation factors