folie 1 synthetic satellite images based on cosmo caroline forster, tobias zinner with contributions...
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Folie 1
Synthetic satellite images based on COSMO
Caroline Forster, Tobias Zinner
with contributions from Christian Keil, Luca Bugliaro, Françoise Faure …
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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)
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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
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• 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)
SynSat – a diagnostic option in COSMO
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Cyclone Veit on 11 Sep 2003
Meteosat-8 SynSat with conv. cloud liquid water
SynSat – a diagnostic option in COSMO
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Cyclone Veit on 11 Sep 2003
Meteosat-8 SynSat with conv. cloud liquid water
Representation of cirrus clouds in COSMO?Controlled by cloud-ice removal via the autoconversion process
SynSat – a diagnostic option in COSMO
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critical ice mixing ratio: zqi0 = 0 kg/kg
Example 1: autoconversion experiments
SynSat – a diagnostic option in COSMO
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critical ice mixing ratio: zqi0 = 2e-5 kg/kg
SynSat – a diagnostic option in COSMO
Example 1: autoconversion experiments
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critical ice mixing ratio: zqi0 = 5e-5 kg/kg
LMSynSat – a diagnostic option in COSMO
Example 1: autoconversion experiments
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Meteosat 7 IR
Example 2: Ensemble best member selection
ensemble members, SynSat
SynSat – a diagnostic option in COSMO
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Advanced synthetic satellite imagery – based on extensive postprocessing of COSMO output
Remote sensing of cloud
properties
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Advanced synthetic satellite imagery – based on extensive postprocessing of COSMO output
Remote sensing of cloud
properties
Truth Quality ???
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Advanced synthetic satellite imagery – based on extensive postprocessing of COSMO output
COSMO: realistic cloud
fields
Radiative transfer model:
IR + VIS + trace gases + aerosol + 3D
Simulated observations
Remote sensing of cloud
properties
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Advanced synthetic satellite imagery – based on extensive postprocessing of COSMO output
COSMO: realistic cloud
fields
Radiative transfer model:
IR + VIS + trace gases + aerosol + 3D
Simulated observations
Remote sensing of cloud
properties
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IR image (similar LMSynSat)
but also visible channels
VIS 600 nm Meteosat VIS 600 nm synthetic
IR 10.8 syntheticIR 10.8 Meteosat
Advanced synthetic satellite imagery
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Meteosat RGB false color (channels 1,2,9)
Synthetic RGB false color (channels 1,2,9)
Advanced synthetic satellite imagery
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Advanced synthetic satellite imagery for validation of remote sensing
cloud cover, truth: COSMO cloud cover, derived from synthetic data
eff. radius, derived from synthetic dataeffective radius, truth: COSMO
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Advanced synthetic satellite imagery
Meteosat-8 False Color RGB, 2004-08-12 synthetic imagery
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The use of synthetic satellite images based on COSMO-DE for thenowcasting of thunderstorms
Caroline Forster
with contributions from
Arnold Tafferner, Tobias Zinner,
Christian Keil and others
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Weather information Weather information at the airportat the airport
Development of an IWFS for the airportsFrankfurt and Munich with the components
wake vortices thunderstorms winter weather
PA, FL, RM, FT, AS, LKDWD, HYDS, Nowcast u.a.
Weather information Weather information at the airportat the airport
Development of an IWFS for the airportsFrankfurt and Munich with the components
wake vortices thunderstorms winter weather
PA, FL, RM, FT, AS, LKDWD, HYDS, Nowcast u.a.
DLR Project Wetter & FliegenDLR Project Wetter & Fliegen main goals and structuremain goals and structure
flight characteristicsflight characteristics
Minimisation of the effects ofturbulence, wake vortices
and thunderstorms through
design and fly-by-wire controls sensor specification information for pilots
FT, RM, PA, FLEADS, Airbus u.a.
flight characteristicsflight characteristics
Minimisation of the effects ofturbulence, wake vortices
and thunderstorms through
design and fly-by-wire controls sensor specification information for pilots
FT, RM, PA, FLEADS, Airbus u.a.
Goal:Goal: Higher security and efficiency of air traffic through
Weather information in the TMA and
Optimisation of the flight characteristics
Structure: Main work packagesStructure: Main work packages
Project period: 01.01.2008 - 31.12.2011Project period: 01.01.2008 - 31.12.2011
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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)
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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
grey: 15 and 30 Min. nowcast
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Weather Forecast User-oriented System Including Object Nowcasting
POLDIRADsurface observations
cloud trackerradar tracker
lightning
User-specifiedTarget WeatherObject
TWO
SYNPOLRADlocal forecast
ensembleforecast
SYNRAD
SYNSATobject
comparison
forecast validationforecast validation
FusionTracking Nowcast (0 -1 hrs)Forecast (1 - 6 hrs)
TWO
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Weather Forecast User-oriented System Including Object Nowcasting
POLDIRADsurface observations
cloud trackerradar tracker
lightning
User-specifiedTarget WeatherObject
TWO
SYNPOLRADlocal forecast
ensembleforecast
SYNRAD
SYNSATobject
comparison
forecast validationforecast validation
FusionTracking Nowcast (0 -1 hrs)Forecast (1 - 6 hrs)
TWO
• 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
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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
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forecast validation by object comparisonCase study 21 July 2007
observed and synthetic objects + COSMO-DE IR10.8 Forecast+ LINET lightning observations
observed objects + METEOSAT IR10.8
+ LINET lightning observations
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forecast validation by object comparisonCase study 21 July 2007
observed and synthetic objects + COSMO-DE IR10.8 Forecast+ LINET lightning observations
observed objects + METEOSAT IR10.8
+ LINET lightning observations
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forecast validation by object comparisonCase study 21 July 2007
observed and synthetic objects + COSMO-DE IR10.8 Forecast+ LINET lightning observations
observed objects + METEOSAT IR10.8
+ LINET lightning observations
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forecast validation by object comparisonCase study 21 July 2007
observed and synthetic objects + COSMO-DE IR10.8 Forecast+ LINET lightning observations
observed objects + METEOSAT IR10.8
+ LINET lightning observations
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forecast validation by object comparisonCase study 21 July 2007
observed and synthetic objects + COSMO-DE IR10.8 Forecast+ LINET lightning observations
observed objects + METEOSAT IR10.8
+ LINET lightning observations
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forecast validation by object comparisonCase study 21 July 2007
observed and synthetic objects + COSMO-DE IR10.8 Forecast+ LINET lightning observations
observed objects + METEOSAT IR10.8
+ LINET lightning observations
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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
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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