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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). - PowerPoint PPT Presentation
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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)
Global NWP models cannot predict extremes of precipitation: need for coupling to LAMs
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
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
Possible Revisions to EPS 2003-2004
1. Twice a day running (12z and 0z) +improved scheduling
2. Dry T42 singular vectors 48hr optimisation Moist T63 singular vectors 24hr optimisation
3. TL255L40TL319-TL399L65
4. Hessian (possibly RRKF) metric
0
10
00
00
10
0 ,
,max
,
,max
00 txAtx
txMMtx
txAtx
txtxtxtx
01
0 txAtxMM
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)
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
Initial time metric and SV structure
Hessian
Total energy
{
temperature at 45N of leading SV optimized for Europe
Initial time metric and SV structureVertical correlations 700hPa, 5leading SVs optimized for Europe
Total energy
Local bulk formula representing the mean effect of neglected scales - driven by resolved scales (eg diffusion)
Residual, =0 in most GCMs. Represent as stochastic noise
=P in ECMWF model where is a stochastic variable?
RXPXFX ];[][
Let X the state vector in an NWP model
Terms retained in the Galerkin basis projection of the underlying pde
PPDX
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
DPDX
)( DPPDX
i
ii
iii