LAMEPS Development and Plan of ALADIN-LACE

Yong Wang et al.Speaker: Harald Seidl

ZAMG, Austria

Thanks: Météo France, LACE, NCEP

ALADIN-LAEF

ALADIN --- Limited Area Ensemble Forecasting

Research on ALADIN-LAEF

Has been focused on:

• Dynamical downscaling• Dealing with uncertainties in analysis• Dealing with uncertainties in LBC• Dealing with uncertainties in model physics

“Generate independently perturbed ICs such that covariance of ENS perturbations approximates the analysis error cov.matrix at the initial time and thus the fcst error cov.matrix at fcst time“ (Bishop 2003)

Overview of LAEF research in ALADIN/LACE

• Austria (& MeteoFr, LACE , NCEP !): Breeding, ETKF, ET, Blending, downscaling, LBC perturbation, physics perturbation

• Croatia: ECMWF EPS downscaling

• Czech: ARPEGE EPS downscaling

• Hungary: ECMWF EPS downscaling, ARPEGE target SV, ALADIN SV

ALADIN-LAEF Configuration

• Hydrostatic, spectral, in vertical• 18km in horizontal, 31 levels in vertical• SLSI time integration, time step 600s• 48h forecast• Bougeault deep convection, Kessler large-scale precipitation, ISBA for surface and soil, etc.....

Dealing with uncertainties in analysis (A1)

Techniques: • Breeding• ETKF: Ensemble Transform Kalman Filter• ET: Ensemble Transform• Blending• Singular vectors

ALADIN-LAEF: Breeding

• 12 hour breeding cycle• u, v, T, q and Ps at each gridpoint/level• Two sided, centering around the control• constant rescaling

Breeding: rescaling

Scaling factor = C / P

Where: P is standard deviation of 850hPa temperature, C is a tuning constant.

ALADIN-LAEF: ETKF

• ETKF + spherical simplex transformation:

post-multiplying the short-term ensemble forecast perturbations by a transformation matrix. This transformation matrix is obtained by solving the error covariance update equation for an optimal assimilation scheme within the ensemble subspace. where T is the transformation matrix, C and are the eigenvectors and eigenvalues of

fTfTffa HPRHHPHPPP 1)(

2/1)(, ICTTXX fa

fTTf HZRHZ 1)(

ETKF

• Fixed observations, ca. 120 stations on 3 levels, interpolated ARPEGE analysis of u,v,T as observation.

• 12 hour cycle, 11 members.

• u, v, T, q and Ps at each gridpoint/level

• Simplex transformation for centering around the control

• Constant rescaling instead of Innovation inflation technique for ETKF

ETKF

The ETKF perturbation is too small, because10 EPS member is much smaller than the number of directions to which the fcst error variance projects.

Solution: inflation technique, which makes short term ensemble spread be consistent with short term differences between fcst and obs. We use the similar technique as in Breeding for up-scaling the ETKF perturbation

ALADIN-LAEF: ETSimilar to ETKF,

where T is the transformation matrix, C and are eigenvectorsand eigenvalues of

is the analysis error covariance matrix.

The up-scaling technique as in Breeding, and spherical simplextransformation are used.

TXX fa 2/1CT

faTf ZPZ1

)(

aP

ALADIN-LAEF: Blending PEARP-SV with ALADIN LAEF Breeding

To combine the large-scale uncertainty from global SV-EPS with the small-scale uncertainty generated by Breeding/ET in LAEF. It is expected that 1). reducing the inconsistency between global and limited area EPS. 2). combining the future uncertainty generated by SV and the uncertainty in the past generated by Breeding. Hypothesis: the small-scale part of IC uncertainty from LAM Bred vector is more realistic than interpolation of global EPS members.

Method: spectral analysis and digital filter.

BLENDING

Results: dealing with errors in analysis

1 month experiment: 01 Feb. to 28 Feb. 2006

11 members, integration up to 48 hours.

Downscaling vs. Global EPS

Downscaling vs. Breeding

ETKF vs. Breeding

First result of Blending

First result of Blending

Dealing with uncertainties in LBC (A2)

Experiments: • Impact of LBC • Impact of inconsistent LBC perturbation with IC perturbation

Investigating the impact of the inconsistency in the generation of the ensemble lateral boundary conditions from global EPS system with the generation of the ensemble initial conditions from the LAM EPS itself.

LAEF coupling with ARPEGE EPS (SV) vs. LAEF coupling with NCEP EPS (Breeding)

Thic k line : b re e d ing , c o ntro l LBC

Thin line : b re e d ing , ARPEG E SV EPS LBC

Impact of perturbation on LBC

Coupling with SV and Breeding

Coupling with SV and Breeding

Dealing with uncertainties in physics (A3)

• Multi-physics parameterization: 11 combinations of different physics parameterizations and tunings in ALADIN have been chosen for downscaling the PEARP members schemes of Lopez, Bougeault, Kain-Fritsch etc....

Results of multi-physics option in LAEF

Results of multi-physics option in LAEF

Preliminary Results on LAMEPS – other LACE countries

Sensitivity tests of ARPEGE target SV (Hungary)

Tests on different target domian and optimisation time:

• Target domains: 5 domains (see figure)

• Optimisation time: 12h and 24h

• Downscaling with the ALADIN model

Sensitivity tests of ARPEGE target SV (Hungary)

• Target domain 1.)• target time: 12h

• target time: 24h

• Target domain 2.)• target time: 12h

• target time: 24h

SENSITIVITY OF GLOBAL SINGULAR

VECTOR COMPUTATION

Sensitivity tests of ARPEGE target SV (Hungary)

Global EPS can be improved with targetting (domain and time)

Dynamical downscaling (Hungary)

Dynamical downscaling of ECMWF EPS• 10 clusters with representative members from 51 and then 102 EPS members, clustering at +60h and 84h• ALADIN integration: 84h• Verification: 3 case studies (precipitation events)

Dynamical downscaling (Hungary)

On the basis of the first subjective and objective evaluations of the ALADIN downscaling ECMWF EPS system, it was found that the ALADIN system in the examined limited number of cases could bring benefit on top of the global ECMWF EPS system

More detailed results and conclusions by A. Horanyi

ALADIN Singular Vectors

ALADIN SV has been implemented, the first result will be presented at the LAMEPS workshop in Vienna, 13-14. Nov. 2006 by Andras Horanyi.

Dynamical downscaling (Croatia)

Dynamical downscaling of ECMWF EPS• 10 clusters with representative members from 51 EPS members• ALADIN integration: 5 days• Clustering on different base parameters was done for Z500, Z700, T850, wind 850, RT500/1000, RT700/1000, omega500, omega700. • Verification: 4 case studies (precipitation/wind events)

Dynamical downscaling (Croatia)

Dynamical downscaling (Croatia)

First results for the chosen severe weather events in Croatia show that downscaling is a useful tool, especially if orography is the triggering effect.

It was also found, that skillfull ECMWF EPS forecasts were not deteriorated through downscaling.

More detailed results and conclusions by S. Ivatek-Sahdan.

Conclusions

LAEF experiments for one winter month have been carried on.

• Simple dynamical downscaling of ARPEGE EPS brings hardly more added value to the global EPS. Introducing multi-physics in the downscaling improves the forecast, in particular in the lower atmosphere.

• LAM-IC perturbation with breeding+LBC perturbation shows certain potential/skill for the short range probabilistic forecast. ETKF with 10 member has very similar performance to Breeding. It is also true for ET perturbation.

• LBC perturbation is very important for keeping spread growing after 12h. LAEF coupling with different global model EPS has much impact on forecast. For the short range probablistic forecast, it seems that LAEF coupling with global Breeding EPS has more benifit than with gloabl SV EPS.

Future plans

ALADIN LAMEPS workshop in Vienna,

13-14 Nov.2006 (register to yong.wang@zamg.ac.at)

Works will be continued:

• Dynamical downscaling of ECMWF EPS (Croatia, Hungary and Austria)

• More detailed investigations on Breeding, ETKF, ET, SV and Blending (Austria and Hungary)

• The impact of the coupling system will be further studied (Austria).

• Post-processing: Bias correction (Austria)

• Common verification plan (Austria and Hungary)

• Participation on international project, like Beijing Olympic Research Demonstration Project (Austria and France)

“Nothing is certain, not even the uncertainty in initial conditions…“

(Martin Bellus)