1. Synthetic satellite images based on COSMO Caroline Forster, Tobias Zinner with contributions from Christian Keil, Luca Bugliaro, Françoise Faure …

2. Synthetic satellite images based on COSMO
SynSat:
IR Meteosat data
using fast approximate radiative transfer solution within COSMO (RTTOV)
Advanced synthetic satellite imagery:
all Meteosat channels
using time-consuming postprocessing of COSMO output (full radiative transfer solution, libRadtran)

3. SynSat – a diagnostic option in COSMO
Remote sensing observations to improve weather forecasts?
Problem: comparability of observed and simulated quantity
radar reflectivity [dBz] vs rainrate [mm/h]
brightness temperature [K] vs cloud cover [%] and cloud top height [m]
Model-to-observation approach
measurements obtained by remote sensing instruments simulated on forecast model fields

4. SynSat – a diagnostic option in COSMO
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
Meteosat first and second generation (IR and WV channels)
(Keil et al., 2006)

5. Meteosat-8 SynSat with conv. cloud liquid water
SynSat – a diagnostic option in COSMO
Cyclone Veit on 11 Sep 2003
Meteosat SynSat with conv. cloud liquid water

6. Meteosat-8 SynSat with conv. cloud liquid water
SynSat – a diagnostic option in COSMO
Cyclone Veit on 11 Sep 2003
Representation of cirrus clouds in COSMO? Controlled by cloud-ice removal via the autoconversion process
Meteosat SynSat with conv. cloud liquid water

7. critical ice mixing ratio: zqi0 = 0 kg/kg
SynSat – a diagnostic option in COSMO
Example 1: autoconversion experiments
critical ice mixing ratio: zqi0 = 0 kg/kg

8. critical ice mixing ratio: zqi0 = 2e-5 kg/kg
SynSat – a diagnostic option in COSMO
Example 1: autoconversion experiments
critical ice mixing ratio: zqi0 = 2e-5 kg/kg

9. critical ice mixing ratio: zqi0 = 5e-5 kg/kg
LMSynSat – a diagnostic option in COSMO
Example 1: autoconversion experiments
critical ice mixing ratio: zqi0 = 5e-5 kg/kg

10. SynSat – a diagnostic option in COSMO
Example 2: Ensemble best member selection
Meteosat 7 IR 
ensemble members, SynSat

11. Remote sensing of cloud properties
Advanced synthetic satellite imagery – based on extensive postprocessing of COSMO output
Remote sensing of cloud properties

12. Remote sensing of cloud properties
Advanced synthetic satellite imagery – based on extensive postprocessing of COSMO output
Truth Quality ???
Remote sensing of cloud properties

13. Advanced synthetic satellite imagery – based on extensive postprocessing of COSMO output
COSMO: realistic cloud fields
Remote sensing of cloud properties
Radiative transfer model: IR + VIS + trace gases + aerosol + 3D
Simulated observations

14. Advanced synthetic satellite imagery – based on extensive postprocessing of COSMO output
COSMO: realistic cloud fields
Remote sensing of cloud properties
Radiative transfer model: IR + VIS + trace gases + aerosol + 3D
Simulated observations

15. Advanced synthetic satellite imagery
IR image (similar LMSynSat)
but also visible channels
IR 10.8 Meteosat
IR 10.8 synthetic
VIS 600 nm Meteosat
VIS 600 nm synthetic

16. Advanced synthetic satellite imagery
Meteosat RGB false color (channels 1,2,9)
Synthetic RGB false color (channels 1,2,9)

17. Advanced synthetic satellite imagery for validation of remote sensing
cloud cover, truth: COSMO
cloud cover, derived from synthetic data
effective radius, truth: COSMO
eff. radius, derived from synthetic data

18. Advanced synthetic satellite imagery
Meteosat False Color RGB, synthetic imagery

19. The use of synthetic satellite images based on COSMO-DE for the nowcasting of thunderstorms Caroline Forster with contributions from Arnold Tafferner, Tobias Zinner, Christian Keil and others

20. DLR Project Wetter & Fliegen main goals and structure
Goal: Higher security and efficiency of air traffic through
Weather information in the TMA and
Optimisation of the flight characteristics
Structure: Main work packages
Weather information
at the airport
Development of an IWFS for the airports
Frankfurt and Munich
with the components
wake vortices
thunderstorms
winter weather
PA, FL, RM, FT, AS, LK
DWD, HYDS, Nowcast u.a.
flight characteristics
Minimisation of the effects of
turbulence, wake vortices
and thunderstorms through
design and fly-by-wire controls
sensor specification
information for pilots
FT, RM, PA, FL
EADS, Airbus u.a.
PAZI 2-L 6,5 PJ (R 4 PJ)
WS-2 7 PJ
Vorhaben Fundament
Project period:

21. Target Weather Object "Cb"
Cb top volumes:
convective turbulence,
lightning
(detected by satellite)
Cb bottom volumes:
hail, icing, lightning,
heavy rain, wind shear, turbulence
(detected by radar)

22. Cb top volumes: Cb-TRAM using METEOSAT data (HRV, IR, WV) case study 04.07.2006
gelb:
onset of convection
orange:
rapid development
rot:
mature thunderstorm
Gewitterzellen und deren Tracks
Vorhersageaspekt betonen!!
grey:
15 and 30 Min.
nowcast

23. Weather Forecast User-oriented System Including Object Nowcasting
WxFUSION
Weather Forecast User-oriented System Including Object Nowcasting
lightning
surface observations
radar tracker
POLDIRAD
cloud tracker
Fusion
User-specified
Target Weather
Object
Tracking
Nowcast (0 -1 hrs)
Forecast (1 - 6 hrs)
TWO
TWO
forecast validation
forecast validation
object
comparison
SYNSAT
SYNPOLRAD
SYNRAD
ensemble
forecast
local
forecast

24. Weather Forecast User-oriented System Including Object Nowcasting
WxFUSION
Weather Forecast User-oriented System Including Object Nowcasting
data fusion through fuzzy logic
output of object attributes (move speed and direction, severity level, level of turbulence...)
forecast of TWOs through a combination of:
Nowcast based on extrapolation methods
&
forecast based on numerical simulations, if they agree with the observation
probabilistic methods
lightning
surface observations
radar tracker
POLDIRAD
cloud tracker
Fusion
User-specified
Target Weather
Object
Tracking
Nowcast (0 -1 hrs)
Forecast (1 - 6 hrs)
TWO
TWO
forecast validation
forecast validation
object
comparison
SYNSAT
SYNPOLRAD
SYNRAD
ensemble
forecast
local
forecast

25. forecast validation by object comparison
Use of COSMO-DE model forecasts from the German Weather Service (DWD)
"synthetic objects":
Cb-TRAM with synthetic satellite data (IR and WV) from the COSMO-DE model
"observed objects":
Cb-TRAM with METEOSAT IR and WV observations (without HRV !!!)
choose a region of interest (e.g. TMA Munich)
determine search box around each observed object
look for synthetic objects within each search box and compare the attributes of the synthetic and observed objects

26. forecast validation by object comparison
Case study 21 July 2007
observed and synthetic objects
+ COSMO-DE IR10.8 Forecast
+ LINET lightning observations
observed objects
+ METEOSAT IR10.8
+ LINET lightning observations

27. forecast validation by object comparison
Case study 21 July 2007
observed and synthetic objects
+ COSMO-DE IR10.8 Forecast
+ LINET lightning observations
observed objects
+ METEOSAT IR10.8
+ LINET lightning observations

28. forecast validation by object comparison
Case study 21 July 2007
observed and synthetic objects
+ COSMO-DE IR10.8 Forecast
+ LINET lightning observations
observed objects
+ METEOSAT IR10.8
+ LINET lightning observations

29. forecast validation by object comparison
Case study 21 July 2007
observed and synthetic objects
+ COSMO-DE IR10.8 Forecast
+ LINET lightning observations
observed objects
+ METEOSAT IR10.8
+ LINET lightning observations

30. forecast validation by object comparison
Case study 21 July 2007
observed and synthetic objects
+ COSMO-DE IR10.8 Forecast
+ LINET lightning observations
observed objects
+ METEOSAT IR10.8
+ LINET lightning observations

31. forecast validation by object comparison
Case study 21 July 2007
observed and synthetic objects
+ COSMO-DE IR10.8 Forecast
+ LINET lightning observations
observed objects
+ METEOSAT IR10.8
+ LINET lightning observations

32. object comparison in WxFUSION using synthetic satellite images from COSMO: current and future work
development of an automatic algorithm that identifies object pairs in the observation and forecast within a pre-defined region
calculate more attributes: intensity, location difference, contingency tables for object-pairs, history (track, size)
determine a criterion for a "good" forecast
choose the best forecast out of an ensemble
inclusion in WxFUSION

33. Synthetic satellite images based on COSMO
SynSat:
operational part of COSMO
a diagnostic option
IR Meteosat images
Use in
model development
ensemble member selection (e.g. thunderstorm now/forecasting)
Advanced synthetic satellite imagery:
postprocessing (including downscaling and elaborate RT)
VIS and IR satellite channels (e.g. Meteosat, MODIS, MSI)
use in remote sensing retrieval development and validation