synthetic satellite images based on cosmo

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Synthetic satellite images based on COSMO

Caroline Forster, Tobias Zinner

with contributions from Christian Keil, Luca Bugliaro, Françoise Faure …

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• 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)


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Cyclone Veit on 11 Sep 2003

Meteosat-8 SynSat with conv. cloud liquid water


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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


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critical ice mixing ratio: zqi0 = 0 kg/kg

Example 1: autoconversion experiments


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critical ice mixing ratio: zqi0 = 2e-5 kg/kg



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critical ice mixing ratio: zqi0 = 5e-5 kg/kg

LMSynSat – a diagnostic option in COSMO


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Meteosat 7 IR

Example 2: Ensemble best member selection

ensemble members, SynSat


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Advanced synthetic satellite imagery – based on extensive postprocessing of COSMO output

Remote sensing of cloud

properties

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properties

Truth Quality ???

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COSMO: realistic cloud

fields

Radiative transfer model:

IR + VIS + trace gases + aerosol + 3D

Simulated observations


properties

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COSMO: realistic cloud

fields

Radiative transfer model:

IR + VIS + trace gases + aerosol + 3D

Simulated observations


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)


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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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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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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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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

synthetic satellite images based on cosmo

Documents

diagnostic option

synthetic satellite

cloudice removal

vs cloud cover

cloud liquid watersynsat

kgkg synsat

ir example

autoconversion process