Lothar (T+42 hours) Figure 4

5-Day ECMWF Ensemble Prediction of Typhoon Rusa

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)

ECMWF EPS – current operational configuration 1. 51 members. TL255L40. Once per day (12z). 25 Initial + Evolved dry singular vectors T42L40. 48 hour optimisation. Energy metric. 2. Stochastic physics

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

ECMWF EPS Skill Spread

BSresol BSrel 10-members

10-members

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

Solid red: EPS; dash blue: MA EPS

Solid red: EPS; dash blue: MA EPS

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

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

Dry vs moist SVs 15/10/87

Dry vs moist SVs 2/8/97

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)

Isopleth of initial pdf Isopleth of forecast pdf

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

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 1948-1999.

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 1948-1999. Total energy

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)

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

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

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