1. SLEPS: Short-range limited-area ensemble prediction system
André Walser, Marco Arpagaus, Jean Quiby, Christof Appenzeller
MeteoSwiss

2. SLEPS: Short-range adaptation of LEPS

3. Global ECMWF EPS ensembles with moist singular vectors
SLEPS: current setup
50+1 members
5 representative members (RMs)
5 Lokal Modell (limited-area) integrations nested into 5 RMs
SLEPS: Short-range limited-area Ensemble Prediction System
5 clusters
Hierarchical Cluster Analysis
area: Europe
fields: 4 variables (U,V,Q,Z) at 3 levels (500, 700, 850) for 3 time-steps (24h, 48h, 72h)
number of clusters: fixed to 5
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 vectors

4. SLEPS: current setup (2)
10 km mesh-size horizontally, 32 vertical levels.

5. SLEPS: Lothar (26.12.99), wind gusts
With moist SVs
With dry SVs

6. SLEPS: Lothar, wind gusts
With moist SVs
With dry SVs

7. SLEPS: Martin (27.12.1999), wind gusts
With moist SVs
With dry SVs

8. SLEPS: Martin, wind gusts
With moist SVs
With dry SVs

9. Plans and questions
Simulation of selected extreme events (mainly heavy precipitation cases) in the Alpine region.
Is dynamical (SLEPS/LEPS) or statistical (neural network) down-scaling of the ECMWF EPS information superior?
Are the down-scaling methods more skilful than the ECMWF EPS itself?
Inter-comparison of
SLEPS/LEPS
dedicated neural network
EFI
→ Objective verification