1. LAMEPS Development and Plan of ALADIN-LACE Yong Wang et al. Speaker: Harald Seidl ZAMG, Austria Thanks: Météo France, LACE, NCEP

2. ALADIN-LAEF ALADIN --- Limited Area Ensemble Forecasting Research on ALADIN-LAEF Has been focused on: Dynamical downscaling Dealing with uncertainties in analysis Dealing with uncertainties in LBC Dealing with uncertainties in model physics “Generate independently perturbed ICs such that covariance of ENS perturbations approximates the analysis error cov.matrix at the initial time and thus the fcst error cov.matrix at fcst time“ (Bishop 2003)

3. Overview of LAEF research in ALADIN/LACE Austria (& MeteoFr, LACE, NCEP !): Breeding, ETKF, ET, Blending, downscaling, LBC perturbation, physics perturbation Croatia: ECMWF EPS downscaling Czech: ARPEGE EPS downscaling Hungary: ECMWF EPS downscaling, ARPEGE target SV, ALADIN SV

5. ALADIN-LAEF Configuration Hydrostatic, spectral,  in vertical 18km in horizontal, 31 levels in vertical SLSI time integration, time step 600s 48h forecast Bougeault deep convection, Kessler large- scale precipitation, ISBA for surface and soil, etc.....

6. Dealing with uncertainties in analysis (A1) Techniques: Breeding ETKF: Ensemble Transform Kalman Filter ET: Ensemble Transform Blending Singular vectors

7. ALADIN-LAEF: Breeding 12 hour breeding cycle u, v, T, q and Ps at each gridpoint/level Two sided, centering around the control constant rescaling

8. Breeding: rescaling Scaling factor = C /  P Where:  P is standard deviation of 850hPa temperature, C is a tuning constant.

9. ALADIN-LAEF: ETKF ETKF + spherical simplex transformation: post-multiplying the short-term ensemble forecast perturbations by a transformation matrix. This transformation matrix is obtained by solving the error covariance update equation for an optimal assimilation scheme within the ensemble subspace. where T is the transformation matrix, C and  are the eigenvectors and eigenvalues of

10. ETKF Fixed observations, ca. 120 stations on 3 levels, interpolated ARPEGE analysis of u,v,T as observation. 12 hour cycle, 11 members. u, v, T, q and Ps at each gridpoint/level Simplex transformation for centering around the control Constant rescaling instead of Innovation inflation technique for ETKF

11. ETKF The ETKF perturbation is too small, because10 EPS member is much smaller than the number of directions to which the fcst error variance projects. Solution: inflation technique, which makes short term ensemble spread be consistent with short term differences between fcst and obs. We use the similar technique as in Breeding for up-scaling the ETKF perturbation

12. ALADIN-LAEF: ET Similar to ETKF, where T is the transformation matrix, C and  are eigenvectors and eigenvalues of is the analysis error covariance matrix. The up-scaling technique as in Breeding, and spherical simplex transformation are used.

13. ALADIN-LAEF: Blending PEARP- SV with ALADIN LAEF Breeding To combine the large-scale uncertainty from global SV-EPS with the small-scale uncertainty generated by Breeding/ET in LAEF. It is expected that 1). reducing the inconsistency between global and limited area EPS. 2). combining the future uncertainty generated by SV and the uncertainty in the past generated by Breeding. Hypothesis: the small-scale part of IC uncertainty from LAM Bred vector is more realistic than interpolation of global EPS members. Method: spectral analysis and digital filter.

14. BLENDING

15. Results: dealing with errors in analysis 1 month experiment: 01 Feb. to 28 Feb. 2006 11 members, integration up to 48 hours.

16. Downscaling vs. Global EPS

17. Downscaling vs. Breeding

18. ETKF vs. Breeding

19. First result of Blending

21. Dealing with uncertainties in LBC (A2) Experiments: Impact of LBC Impact of inconsistent LBC perturbation with IC perturbation Investigating the impact of the inconsistency in the generation of the ensemble lateral boundary conditions from global EPS system with the generation of the ensemble initial conditions from the LAM EPS itself. LAEF coupling with ARPEGE EPS (SV) vs. LAEF coupling with NCEP EPS (Breeding)

22. Impact of perturbation on LBC

23. Coupling with SV and Breeding

25. Dealing with uncertainties in physics (A3) Multi-physics parameterization: 11 combinations of different physics parameterizations and tunings in ALADIN have been chosen for downscaling the PEARP members schemes of Lopez, Bougeault, Kain-Fritsch etc....

26. Results of multi-physics option in LAEF

28. Preliminary Results on LAMEPS – other LACE countries

29. Sensitivity tests of ARPEGE target SV (Hungary) Tests on different target domian and optimisation time: Target domains: 5 domains (see figure) Optimisation time: 12h and 24h Downscaling with the ALADIN model

30. Sensitivity tests of ARPEGE target SV (Hungary)

31. Target domain 1.) target time: 12h target time: 24h Target domain 2.) target time: 12h target time: 24h SENSITIVITY OF GLOBAL SINGULAR VECTOR COMPUTATION

32. Sensitivity tests of ARPEGE target SV (Hungary) Global EPS can be improved with targetting (domain and time)

33. Dynamical downscaling (Hungary) Dynamical downscaling of ECMWF EPS 10 clusters with representative members from 51 and then 102 EPS members, clustering at +60h and 84h ALADIN integration: 84h Verification: 3 case studies (precipitation events)

34. Dynamical downscaling (Hungary) On the basis of the first subjective and objective evaluations of the ALADIN downscaling ECMWF EPS system, it was found that the ALADIN system in the examined limited number of cases could bring benefit on top of the global ECMWF EPS system More detailed results and conclusions by A. Horanyi

35. ALADIN Singular Vectors ALADIN SV has been implemented, the first result will be presented at the LAMEPS workshop in Vienna, 13-14. Nov. 2006 by Andras Horanyi.

36. Dynamical downscaling (Croatia) Dynamical downscaling of ECMWF EPS 10 clusters with representative members from 51 EPS members ALADIN integration: 5 days Clustering on different base parameters was done for Z500, Z700, T850, wind 850, RT500/1000, RT700/1000, omega500, omega700. Verification: 4 case studies (precipitation/wind events)

37. Dynamical downscaling (Croatia)

38. First results for the chosen severe weather events in Croatia show that downscaling is a useful tool, especially if orography is the triggering effect. It was also found, that skillfull ECMWF EPS forecasts were not deteriorated through downscaling. More detailed results and conclusions by S. Ivatek- Sahdan.

39. Conclusions LAEF experiments for one winter month have been carried on. Simple dynamical downscaling of ARPEGE EPS brings hardly more added value to the global EPS. Introducing multi-physics in the downscaling improves the forecast, in particular in the lower atmosphere. LAM-IC perturbation with breeding+LBC perturbation shows certain potential/skill for the short range probabilistic forecast. ETKF with 10 member has very similar performance to Breeding. It is also true for ET perturbation. LBC perturbation is very important for keeping spread growing after 12h. LAEF coupling with different global model EPS has much impact on forecast. For the short range probablistic forecast, it seems that LAEF coupling with global Breeding EPS has more benifit than with gloabl SV EPS.

40. Future plans ALADIN LAMEPS workshop in Vienna, 13-14 Nov.2006 (register to yong.wang@zamg.ac.at) Works will be continued: Dynamical downscaling of ECMWF EPS (Croatia, Hungary and Austria) More detailed investigations on Breeding, ETKF, ET, SV and Blending (Austria and Hungary) The impact of the coupling system will be further studied (Austria). Post-processing: Bias correction (Austria) Common verification plan (Austria and Hungary) Participation on international project, like Beijing Olympic Research Demonstration Project (Austria and France)

41. “Nothing is certain, not even the uncertainty in initial conditions…“ (Martin Bellus)