Object oriented best member selection in a regional ensemble forecasting system
This presentation is the property of its rightful owner.
Sponsored Links
1 / 16

Object-Oriented Best Member Selection in a Regional Ensemble Forecasting System PowerPoint PPT Presentation


  • 57 Views
  • Uploaded on
  • Presentation posted in: General

Object-Oriented Best Member Selection in a Regional Ensemble Forecasting System. Christian Keil and George Craig Institut für Physik der Atmosphäre DLR Oberpfaffenhofen, Germany. Regional Ensemble Prediction System. COSMO-LEPS regional ensemble (ARPA SMR)

Download Presentation

Object-Oriented Best Member Selection in a Regional Ensemble Forecasting System

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Object oriented best member selection in a regional ensemble forecasting system

Object-Oriented Best Member Selection in a Regional Ensemble Forecasting System

Christian Keil and George Craig

Institut für Physik der Atmosphäre

DLR Oberpfaffenhofen, Germany


Regional ensemble prediction system

Regional Ensemble Prediction System

  • COSMO-LEPS regional ensemble (ARPA SMR)

  • Identify ten clusters from ECMWF 51 member ensemble

  • 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


Regional ensemble prediction system1

Regional Ensemble Prediction System

  • Main components:

  • COSMO-LEPS: based on ECMWF EPS providing initial and boundary conditions and Lokal-Modell (LM Δx=7km)

  • LMSynSat: forward operator to compute synthetic satellite imagery in LM

  • 3.Objective Pattern Recognition Algorithm using Pyramidal Image Matching


Cosmo leps case study 9 july 2002

COSMO-LEPS case-study: 9 July 2002

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


Generation of synthetic satellite images in lm lmsynsat

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)


Case study with cosmo leps 9 july 2002

Case Study with COSMO-LEPS: 9 July 2002

Meteosat 7 IR 16:00 UTC

Lokal Modell: all 10 clusters


Pyramidal image matching

Pyramidal Image Matching

  • Project observed and simulated images to same grid

  • 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


Image matching bt 20 c and coarse grain

Image Matching: BT< -20°C and coarse grain

Meteosat 7 IR

1 Pixelelement = 8x8 LM GP


Image matching bt 20 c and coarse grain1

Image Matching: BT< -20°C and coarse grain

Observed

Model Cluster 7


Image matching bt 20 c and coarse grain2

DisplacementVectors

Image Matching: BT< -20°C and coarse grain

Observed

Model Cluster 7


Image matching successively finer scales

Image Matching: successively finer scales


Image matching successively finer scales1

Image Matching: successively finer scales


Displacement vectors and matched image

Displacement vectors and matched image


Ranking using different quality measures

Ranking using different Quality Measures

  • Rank 1 2 3 4 5 6 7 8 9 10 corr.

  • subjective2 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


A new quality measure

A new Quality Measure

FQI = 0.33 * [ nordispl + (1-LM/Sat)+ + (1-corr)]

bad

good


Conclusions

Conclusions

  • Pyramidal image matching provides a plausible measure of forecast error (consistent with subjective rankings)

  • COSMO-LEPS cluster populations are a poor indicator of local skill

  • 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


  • Login