1. Institut für Physik der Atmosphäre Institut für Physik der Atmosphäre Object-Oriented Best Member Selection in a Regional Ensemble Forecasting System Christian Keil and George Craig Institut für Physik der Atmosphäre DLR Oberpfaffenhofen, Germany

2. Institut für Physik der Atmosphäre Regional Ensemble Prediction System COSMO-LEPS regional ensemble (ARPA SMR) 1.Identify ten clusters from ECMWF 51 member ensemble 2.Use a representative member from each cluster to drive a regional model (DWD Lokal Model) To make use of forecast ensemble, need to weight members  equally probable  use cluster populations, or  use most recent data (e.g. satellite imagery) But, for local severe weather, phase errors may dominate, so use a nonlinear pattern recognition algorithm

3. Institut für Physik der Atmosphäre Main components: 1.COSMO-LEPS: based on ECMWF EPS providing initial and boundary conditions and Lokal-Modell (LM Δx=7km ) 2.LMSynSat: forward operator to compute synthetic satellite imagery in LM 3.Objective Pattern Recognition Algorithm using Pyramidal Image Matching Regional Ensemble Prediction System

4. Institut für Physik der Atmosphäre Clustering of 1 EPSs fc range +48..60h (2002070912-00) using 4 discriminating variables at 3 pressure levels (u,v,Φ,q at 500/700/850 hPa): Clustering method -----> COMPLETE LINKAGE Selection mode --------> MINIMIZE INT/EXT RATIO Ensemble --------------> 1 Initial Date ----------> 2002 7 7 12 UTC Forecast range (hours) -> 48 - 60 Area Limits (N/S/W/E) --> 60.0 30.0 -10.0 30.0 Number of clusters ----> 10 Explained Variance(%) -> 42.8 Cluster ---------------> 1 2 3 4 5 6 7 8 9 10 Size ------------------> 6 8 10 6 6 4 4 5 1 1 Internal variance(%) --> 5.8 9.8 12.3 6.9 6.8 4.6 4.6 6.5.0.0 Radius -----------------> 12.3 13.8 13.8 13.3 13.3 13.4 13.4 14.2.0.0 CL 1: ( 5) 0 5 17 24 40 41 CL 2: ( 1) 1 4 9 11 18 32 33 49 CL 3: ( 31) 2 3 10 12 26 28 31 34 46 50 CL 4: ( 39) 6 16 22 29 39 42 CL 5: ( 43) 7 13 36 38 43 48 CL 6: ( 45) 8 25 27 45 CL 7: ( 44) 14 35 37 44 CL 8: ( 15) 15 20 21 30 47 CL 9: ( 19) 19 CL 10: ( 23) 23 COSMO-LEPS case-study: 9 July 2002

5. Institut für Physik der Atmosphäre Generation of synthetic satellite images in LM: LMSynSat RTTOV-7 radiative transfer model (Saunders et al, 1999) Input: 3D fields: T,qv,qc,qi,qs,clc,ozone surface fields: T_g, T_2m, qv_2m, fr_land Output: cloudy/clear-sky brightness temperatures for Meteosat7 (IR and WV channels) and Meteosat8 (eight channels) (Keil et al, 2005)

6. Institut für Physik der Atmosphäre Lokal Modell: all 10 clusters Meteosat 7 IR 16:00 UTC Case Study with COSMO-LEPS: 9 July 2002

7. Institut für Physik der Atmosphäre Pyramidal Image Matching 1.Project observed and simulated images to same grid 2.Coarse-grain both images by pixel averaging, then compute displacement vector field that minimizes the total squared error in brightness temperature; search area +/- 2 pixel elements 3.Repeat step 2 at successively finer scales 4.Displacement vector for every pixel results from the sum over all scales

8. Institut für Physik der Atmosphäre Image Matching: BT< -20°C and coarse grain Meteosat 7 IR 1 Pixelelement = 8x8 LM GP

9. Institut für Physik der Atmosphäre Image Matching: BT< -20°C and coarse grain Model Cluster 7Observed

10. Institut für Physik der Atmosphäre Displacement Vectors Image Matching: BT< -20°C and coarse grain Model Cluster 7Observed

11. Institut für Physik der Atmosphäre Image Matching: successively finer scales

12. Institut für Physik der Atmosphäre Image Matching: successively finer scales

13. Institut für Physik der Atmosphäre Displacement vectors and matched image

14. Institut für Physik der Atmosphäre Rank 1 2 3 4 5 6 7 8 9 10 corr. subjective 2 10 4 7 9 1 5 6 8 30.85 new measure 7 2 9 4 10 1 8 6 3 5 0.77 population 3 2 4 1 5 7 6 8 9 10 -0.34 Magnitude of displacement vectors consistent with subjective ranking Cluster population shows no correlation Ranking using different Quality Measures

15. Institut für Physik der Atmosphäre A new Quality Measure FQI = 0.33 * [ nordispl + (1-LM/Sat) + + (1-corr)] good bad

16. Institut für Physik der Atmosphäre normalized mean displacement vector Designing a Quality Measure (i)

17. Institut für Physik der Atmosphäre cloud amount (BT< -20°C) of Meteosat and LM Designing a Quality Measure (ii)

18. Institut für Physik der Atmosphäre spatial correlation after matching Designing a Quality Measure (iii)

19. Institut für Physik der Atmosphäre Rank Correlation with different lead times 1h 3h 6h 9h

20. Institut für Physik der Atmosphäre IR sequence for 9 July 2002 Meteosat 7 IR LM fc + 45...72h

21. Institut für Physik der Atmosphäre Weighted displacement vs time wdis=fct(displ,LM_cloud,corr_matched) good bad FrontalConvectiveFrontal

22. Institut für Physik der Atmosphäre Conclusions 1.Pyramidal image matching provides a plausible measure of forecast error (consistent with subjective rankings) 2.COSMO-LEPS cluster populations are a poor indicator of local skill 3.Persistence of skill for about 12 hours owing to change of weather regime in region Future: adaptive forecasting system: stochastic physics, assimilation of MSG and radar data