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Institut für
Physik der Atmosphäre
Christian Keil
Institut für Physik der AtmosphäreDLR 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 COSMO2. 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-scaleprocesses, 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
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 10.8
1 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<Tthreshold) 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
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 frameworkof a regional ensemble system (COSMO-LEPS)
