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)