1. Lothar (T+42 hours)
Figure 4

2. 5-Day ECMWF Ensemble Prediction of Typhoon Rusa

3. Global NWP models cannot predict extremes of precipitation: need for coupling to LAMs
Extreme rainfall as a function of spatial scale (observational study: Olsson et al, 1999)
Figure 3
EPS cannot resolve circulation features in this range (cf lack of k-5/3 spectrum in model)

4. ECMWF EPS – current operational configuration
members. TL255L40. Once per day (12z). 25 Initial + Evolved dry singular vectors T42L hour optimisation. Energy metric.
2. Stochastic physics

5. spread
D+2
control error
D+4
D+7

6. ECMWF EPS
Skill
Spread

7. BSresol
BSrel
10-members

8. 10-members

9. Multi-analysis EPS MA EPS: 6-member ensemble
Compare with EPS for 500 hPa height, spring 2002 (90 cases)
Spread less than EPS
Worse probability scores than EPS

10. Solid red: EPS; dash blue: MA EPS

11. Solid red: EPS; dash blue: MA EPS

12. Possible Revisions to EPS 2003-2004
Twice a day running (12z and 0z) +improved scheduling
Dry T42 singular vectors 48hr optimisation  Moist T63 singular vectors 24hr optimisation
3. TL255L40TL319-TL399L65
4. Hessian (possibly RRKF) metric

13. Dry vs moist SVs 27/12/99. M.Coutinho, Reading U
24-hr optimisation
T63 resolution

14. Dry vs moist SVs
15/10/87

15. Dry vs moist SVs
2/8/97

18. To find the initial perturbation, consistent with the statistics of initial error, which evolves into the perturbation with largest total energy
Singular vectors of M
In principle, A is the analysis error covariance matrix. In practice, A is approximated by a simplified metric (eg total energy)

19. Isopleth of initial pdf
Isopleth of forecast pdf

20. Initial time metric and SV structure
Singular vectors for T1/Lothar computed with different initial time metrics
total energy, Hessian metric with/without observations
optimization period: 24 Dec 1999, 12 UT +48h

21. Initial time metric and SV structure
temperature at 45N of leading SV optimized for Europe {
Hessian
Total energy
Two slightly different NAO indices are shown; the difference which is the choice of the southern station (Azores vs. Gibraltar.) The figure on the right-hand side shows the typical anomalous SLP pattern for a positive phase of the NAO. It was obtained by regressing SLP anomalies onto the normalized NAO index using NCEP/NCAR reanalysis data for the period

22. Initial time metric and SV structure
Vertical correlations 700hPa, 5leading SVs optimized for Europe
Two slightly different NAO indices are shown; the difference which is the choice of the southern station (Azores vs. Gibraltar.) The figure on the right-hand side shows the typical anomalous SLP pattern for a positive phase of the NAO. It was obtained by regressing SLP anomalies onto the normalized NAO index using NCEP/NCAR reanalysis data for the period
Total energy

23. Let X the state vector in an NWP model
Terms retained in the Galerkin basis projection of the underlying pde
Residual, =0 in most GCMs. Represent as stochastic noise
=P in ECMWF model where  is a stochastic variable?
Local bulk formula representing the mean effect of neglected scales - driven by resolved scales (eg diffusion)

24. ECMWF stochastic physics scheme(s)
 is a stochastic variable, drawn from a uniform distribution in [-0.5, 0.5], constant over time intervals of 6hrs and over 10x10 lat/long boxes ii
iii

25. 2-day forecasts differing only in realisations of the stochastic physics parametrisation

26. Stochastic Physics has a positive impact on ensemble skill
Area under ROC curve. E: precip>40mm/day. Winter- top curves. Summer – bottom curves
Stoch phys
No stoch phys
Buizza et al, 1999