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Institut für Physik der Atmosphäre Institut für Physik der Atmosphäre Object-Oriented Best Member Selection in a Regional Ensemble Forecasting System Christian.

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Presentation on theme: "Institut für Physik der Atmosphäre Institut für Physik der Atmosphäre Object-Oriented Best Member Selection in a Regional Ensemble Forecasting System Christian."— Presentation transcript:

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 h ( ) 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 > UTC Forecast range (hours) -> Area Limits (N/S/W/E) --> Number of clusters ----> 10 Explained Variance(%) -> 42.8 Cluster > Size > Internal variance(%) --> Radius > CL 1: ( 5) CL 2: ( 1) CL 3: ( 31) CL 4: ( 39) CL 5: ( 43) CL 6: ( 45) CL 7: ( 44) CL 8: ( 15) 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 corr. subjective new measure population 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 h

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


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