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

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Presentation transcript:

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

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)

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

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)

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

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-8 SynSat with conv. cloud liquid water

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

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

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

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

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

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

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

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

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

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

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

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

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

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: 01.01.2008 - 31.12.2011

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)

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

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

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

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

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

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

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

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

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

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

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

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