The Impact of Moist Singular Vectors and Horizontal Resolution on Short-Range Limited-Area Ensemble Forecasts for Extreme Weather Events

A. Walser1)

M. Arpagaus1)

M. Leutbecher2)

C. Appenzeller1)

1)MeteoSwiss, Zurich

2)ECMWF, Reading, GB

Perturbations of initial conditions

Perturbations should match the uncertainties in the initial conditions.

Ideally, an ensemble span the entire range of possible solutions.

Initial perturbations using “moist” singular vectors (SVs) might account for a more reliable spread in the short-range.

Perturbations of initial conditions

Perturbations should match the uncertainties in the initial conditions.

Ideally, an ensemble span the entire range of possible solutions.

Initial perturbations using “moist” singular vectors (SVs) might account for a more reliable spread in the short-range.

Moist vs. operational singular vectorsCoutinho et al. (2004)

‚opr‘ SVs (T42L31, OTI 48 h): linearized physics package with surface drag simple vertical diffusion

‚moist‘ SVs (T63L31, OTI 24 h): linearized physics package includes additionally:

gravity wave drag long-wave radiation deep cumulus convection large-scale condensation

moist SVs: use of moist processes in SV calculation, but same norm (‚total energy norm‘) no humidity perturbations.

SLEPS

SLEPS: Short-range Limited-area Ensemble Prediction System

Variant of the operational COSMO-LEPS

Motivation: Early warnings for extreme weather events

dynamical

downscaling

Global ensemble Limited-area ensemble 51 ensemble members

LM with 10 km grid-spacing and 32 levels

72-h forecasts

IFS members use „moist“ singular vectorsIFS (ECMWF), ∆x~80 km,

moist SVsLM, x~10 km

SLEPS simulations

LM 3.92 ensembles using Brasseur (2001) wind gust formulation: ∆x ~80 km (as ECMWF EPS) ∆x ~10 km but ECMWF EPS topography ∆x ~10 km (as COSMO-LEPS)

Storm Lothar: 26 December 1999 moist SVs ECMWF EPS SLEPS 19991224 00 UTC, + 72 h opr SVs ECMWF EPS SLEPS 19991224 00 UTC, + 72 h

Storm Martin: 27/28 December 1999 moist SVs ECMWF EPS SLEPS 19991226 00 UTC, + 72 h opr SVs ECMWF EPS SLEPS 19991226 00 UTC, + 72 h

Wind gusts storm Lothar (26.12.1999)

LM analysis with nudging: Proxy for observations

SLEPS with opr SVs, x~80 km

2-day forecast max. wind gusts storm Lothar (1)

SLEPS with opr SVs, ECMWF EPS topography

2-day forecast max. wind gusts storm Lothar (2)

SLEPS with moist SVs, ECMWF EPS topography

2-day forecast max. wind gusts storm Lothar (3)

SLEPS with moist SV

2-day forecast max. wind gusts storm Lothar (4)

SLEPS with moist SV, only 10 members (as COSMO-LEPS)

2-day forecast max. wind gusts storm Lothar (5)

Wind gusts storm Martin (27.-28.12.1999)

LM analysis with nudging: Proxy for observations

SLEPS mit opr SVs, x~80 km

2-day forecast max. wind gusts storm Martin (1)

SLEPS with opr SVs, ECMWF EPS topography

2-day forecast max. wind gusts storm Martin (2)

SLEPS with moist SVs, ECMWF EPS topography

2-day forecast max. wind gusts storm Martin (3)

SLEPS with moist SVs

2-day forecast max. wind gusts storm Martin (4)

SLEPS with moist SVs, only 10 members (as COSMO-LEPS)

2-day forecast max. wind gusts storm Martin (5)

Conclusions

High-Resolution ensemble predictions have potential to detect storms earlier and more reliably in the future.

Contribution from moist singular vectors is crucial.

Questions?

Extra Slides

Parameterization for 10m wind gusts

LM („operational“):3 x double turbulent kinetic energy in Prandtl-Layer: U* : Friction velocity

Brasseur wind gust formulation(Mon. Wea. Rev. 129, 5-25, 2001)

)()(max 22pp zVzUWg

SLEPS clustering

50+1 members

10 representative members (RMs)

10 Lokal Modell (limited-area) integrations nested into 5 RMsSLEPS: Short-range limited-area Ensemble Prediction System10 Lokal Modell (limited-area) integrations nested into 5 RMs

SLEPS: Short-range limited-area Ensemble Prediction System

10 clusters

Hierarchical Cluster Analysisarea: Europe

fields: 4 variables (U,V,Q,Z) at 3 levels (500, 700, 850) for 3 time steps (24h, 48h, 72 h),

number of clusters: fixed to 10

Hierarchical Cluster Analysisarea: Europe

fields: 4 variables (U,V,Q,Z) at 3 levels (500, 700, 850) for 3 time steps (24h, 48h, 72 h),

number of clusters: fixed to 10

Representative Member Selection one per cluster:

member nearest (3D) to the mean of its own cluster AND most distant to the other clusters’

means

Representative Member Selection one per cluster:

member nearest (3D) to the mean of its own cluster AND most distant to the other clusters’

means

Global ECMWF EPS ensembles with moist singular vectorsGlobal ECMWF EPS ensembles with moist singular vectors