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Synthetic satellite images based on COSMO

Synthetic satellite images based on COSMO Caroline Forster, Tobias Zinner with contributions from Christian Keil, Luca Bugliaro, Fran ço ise Faure …. Synthetic satellite images based on COSMO. SynSat: IR Meteosat data

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Synthetic satellite images based on COSMO

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  1. Synthetic satellite images based on COSMOCaroline Forster, Tobias Zinnerwith contributions from Christian Keil, Luca Bugliaro, Françoise Faure …

  2. Synthetic satellite images based on COSMO • SynSat: • IR Meteosat data • using fastapproximate radiative transfer solution within COSMO (RTTOV) • Advanced synthetic satellite imagery: • all Meteosat channels • using time-consumingpostprocessing 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. SynSat – a diagnostic option in COSMO Cyclone Veit on 11 Sep 2003 Meteosat-8 SynSat with conv. cloud liquid water

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

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

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

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

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

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

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

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

  14. Advanced synthetic satellite imagery – based on extensive postprocessing of COSMO output Remote sensing of cloud properties COSMO: realistic cloud fields 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 eff. radius, derived from synthetic data effective radius, truth: COSMO

  18. Advanced synthetic satellite imagery Meteosat-8 False Color RGB, 2004-08-12 synthetic imagery

  19. The use of synthetic satellite images based on COSMO-DE for thenowcasting of thunderstormsCaroline Forsterwith 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 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. Structure: Main work packages 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. Project period: 01.01.2008 - 31.12.2011

  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 grey: 15 and 30 Min. nowcast

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

  24. Tracking Nowcast (0 -1 hrs) Forecast (1 - 6 hrs) TWO 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 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 andobserved objects

  26. forecast validation by object comparison Case study 21 July 2007 observedand syntheticobjects + COSMO-DE IR10.8 Forecast + LINET lightning observations observedobjects + METEOSAT IR10.8 + LINET lightning observations

  27. forecast validation by object comparison Case study 21 July 2007 observedand syntheticobjects + COSMO-DE IR10.8 Forecast + LINET lightning observations observedobjects + METEOSAT IR10.8 + LINET lightning observations

  28. forecast validation by object comparison Case study 21 July 2007 observedand syntheticobjects + COSMO-DE IR10.8 Forecast + LINET lightning observations observedobjects + METEOSAT IR10.8 + LINET lightning observations

  29. forecast validation by object comparison Case study 21 July 2007 observedand syntheticobjects + COSMO-DE IR10.8 Forecast + LINET lightning observations observedobjects + METEOSAT IR10.8 + LINET lightning observations

  30. forecast validation by object comparison Case study 21 July 2007 observedand syntheticobjects + COSMO-DE IR10.8 Forecast + LINET lightning observations observedobjects + METEOSAT IR10.8 + LINET lightning observations

  31. forecast validation by object comparison Case study 21 July 2007 observedand syntheticobjects + COSMO-DE IR10.8 Forecast + LINET lightning observations observedobjects + 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

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