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Lothar (T+42 hours)

Lothar (T+42 hours). 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).

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Lothar (T+42 hours)

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  1. Lothar (T+42 hours)

  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) EPS cannot resolve circulation features in this range (cf lack of k-5/3 spectrum in model)

  4. 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

  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

  16. 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)

  17. Isopleth of initial pdf Isopleth of forecast pdf

  18. 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

  19. Initial time metric and SV structure temperature at 45N of leading SV optimized for Europe { Hessian Total energy

  20. Initial time metric and SV structure Vertical correlations 700hPa, 5leading SVs optimized for Europe Total energy

  21. 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)

  22. ECMWF stochastic physics scheme(s) i  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

  23. 2-day forecasts differing only in realisations of the stochastic physics parametrisation

  24. 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

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