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Institut für Physik der Atmosphäre Christian Keil Institut für Physik der Atmosphäre DLR Oberpfaffenhofen Germany Forecast Quality Control Applying an.

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Presentation on theme: "Institut für Physik der Atmosphäre Christian Keil Institut für Physik der Atmosphäre DLR Oberpfaffenhofen Germany Forecast Quality Control Applying an."— Presentation transcript:

1 Institut für Physik der Atmosphäre Christian Keil Institut für Physik der Atmosphäre DLR Oberpfaffenhofen Germany Forecast Quality Control Applying an Object-Oriented Approach Using Remote Sensing Information

2 Institut für Physik der Atmosphäre Motivation Meso-scale forecasting at high spatial resolution increases the variability of forecast weather phenomena, e.g. precipitation and cloud structures, and render the comparison of forecast fields with observations more difficult. A common problem of meso-scale forecast fields often stems from conditions where a weather system is properly developed in the model but improperly positioned. For misplacement errors, a direct measure of the displacement is likely to be more valuable than traditional measures, such as RMS error.

3 Institut für Physik der Atmosphäre Aim Here, a displacement measure is developed, that builds crucially on the pattern information contained in satellite observations. Tools 1.Lokal-Modell (LM; Δx=7km) of COSMO 2.Forward operator generating synthetic satellite imagery in LM (LMSynSat) 3.Objective Pattern Recognition Algorithm using Pyramidal Image Matching

4 Institut für Physik der Atmosphäre Lokal-Modell non-hydrostatic 325x325x35 GP meshsize 7km Param. subgrid-scale processes, i.e. moist convection (Tiedtke) grid-scale precip incl. cloud ice (since 09/03) progn. precipitation (since 04/04) progn. variables: u,v,w,T,p',qv,qc,qi,qs,qr

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 Meteosat 8 (MSG) observations on 12 Aug 2004

7 Institut für Physik der Atmosphäre Meteosat 8 IR 10.8 versus Lokal-Modell

8 Institut für Physik der Atmosphäre Meteosat 8 (MSG) IR 10.8 versus Lokal-Modell Histogram of Brightness Temp.

9 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 maximizes correlation in brightness temperature; search area +/- 2 grain size 3.Repeat step 2 at successively finer scales 4.Displacement vector for every pixel results from the sum over all scales

10 Institut für Physik der Atmosphäre Image Matching: BT< -20°C and coarse grain Meteosat 8 IR Pixelelement = 16x16 LM GP

11 Institut für Physik der Atmosphäre Image Matching: BT< -20°C and coarse grain Lokal-ModellObserved Displacement vectors 1 Pixelelement = 16x16 LM GP

12 Institut für Physik der Atmosphäre Image Matching: successively finer scales 1 Pixelelement = 8x8 LM GP

13 Institut für Physik der Atmosphäre Image Matching: successively finer scales 1 Pixelelement = 4x4 LM GP

14 Institut für Physik der Atmosphäre Displacement vectors and matched image

15 Institut für Physik der Atmosphäre cloud amount (BT

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

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

18 Institut für Physik der Atmosphäre directional variance of displacement vectors, i.e. divergence or convergence of vector field Designing a Quality Measure (iv)

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

20 Institut für Physik der Atmosphäre Summary & Outlook 1.Objective Forecast Quality Control with Meteosat observations is possible using * LMSynSat and * Pyramidal Image Matching Algorithm 2.Results presented for 12 August 2004 case study * LM seems to underestimate (high) cloud amount * Timing ok 3. Usage of radar data 4. New quality measure will be applied in the framework of a regional ensemble system (COSMO-LEPS)


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