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Forecast Quality Control Applying an Object-Oriented Approach Using Remote Sensing Information

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

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Forecast Quality Control Applying an Object-Oriented Approach Using Remote Sensing Information

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

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

  4. 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. 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. Meteosat 8 (MSG) observations on 12 Aug 2004

  7. Meteosat 8 IR 10.8 versus Lokal-Modell

  8. Pyramidal Image Matching • Project observed and simulated images to same grid • 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

  9. Image Matching: BT< -20°C and coarse grain Meteosat 8 IR 10.8 1 Pixelelement = 16x16 LM GP

  10. Image Matching: BT< -20°C and coarse grain Displacement vectors Observed Lokal-Modell 1 Pixelelement = 16x16 LM GP

  11. Image Matching: successively finer scales 1 Pixelelement = 8x8 LM GP

  12. Image Matching: successively finer scales 1 Pixelelement = 4x4 LM GP

  13. Displacement vectors and matched image

  14. Designing a Quality Measure (i) • cloud amount (BT<Tthreshold) of Meteosat and LM M8 LM

  15. Designing a Quality Measure (ii) • normalized mean displacement vector

  16. Designing a Quality Measure (iii) • spatial correlation after matching

  17. A new Quality Measure (iv) FQI = 0.33 * [ (1-LM/Sat)+ + nordispl + (1-corr)]

  18. Summary & Outlook • Objective Forecast Quality Control with Meteosat • observations is possible using • * LMSynSat and • * Pyramidal Image Matching Algorithm • 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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