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

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.

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

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

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)

Institut für Physik der Atmosphäre Meteosat 8 (MSG) observations on 12 Aug 2004

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

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

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

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

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

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

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

Institut für Physik der Atmosphäre Displacement vectors and matched image

Institut für Physik der Atmosphäre cloud amount (BT<T threshold ) of Meteosat and LM Designing a Quality Measure (i) M8 LM

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

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

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

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

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)