1. USING OF METEOSAT SECOND GENERATION HIGH RESOLUTION VISIBLE DATA FOR THE IMPOVEMENT OF THE RAPID DEVELOPPING THUNDERSTORM PRODUCT Oleksiy Kryvobok Ukrainian HydroMeteorological Institute Kyiv, Ukraine Stephane Senesi Meteo-France, DPREVI/PI Toulouse, France Christophe Morel Meteo-France, DPREVI/PI Toulouse, France

2. The RDT product is an object – oriented diagnostic which provides support to forecasters for real time analysis and automatic warnings of convection. It is produced by RDT software, which use as input data infrared images of the 10.8 (IR10.8) channel of geostationary satellite. Brightness temperature field measured by satellite is used in RDT product for description of convective objects with relevant properties (size, movement, minimum temperature, cooling rates area extentsion rate). See poster of Frederic Autones.

3. The spatial resolution of one of the MSG channels, the High Resolution Visible channel (HRVIS), is 1 km at sub-satellite point (against 3 km for the others channels). This should allow the HRVIS channel to describe smaller clouds than the IR10.8 channel and improve an earliness of first detection of convective systems during their growing phase in order to be able to warn and provide information to forecaster when severe weather may occur. The spatial resolution of one of the MSG channels, the High Resolution Visible channel (HRVIS), is 1 km at sub-satellite point (against 3 km for the others channels). This should allow the HRVIS channel to describe smaller clouds than the IR10.8 channel and improve an earliness of first detection of convective systems during their growing phase in order to be able to warn and provide information to forecaster when severe weather may occur. channel Band channel type Nominal Centre wavelength (μm) Spectral Bandwidth Spectral Bandwidth (μm) Spatial resolution at SSP, Spatial resolution at SSP, (km) HRV Visible High Resolution 0.75 0.6 – 0.9 1

4. CLOUD FRACTION ESTIMATION USING VISIBLE DATA R VIS =N  R c +(1-N)  R clear where R VIS - measured reflectance, R c - top of atmosphere (TOA) reflectance of cloud, R clear - TOA reflectance of clear sky surface

5. Clear sky pixel TOA reflectance CP Domain around the Cloudy Pixel ( CP ) where satellite and sun angles are almost constant.  Nearby Clear Sky Pixel Method

6. Clear sky pixel TOA reflectance Reflectance of surfaceAtmospheric state Land cover map Visible reflectance map Topography map Monthly integrated water vapour content Nearby Clear Sky Pixel Method

7. Clear sky pixel TOA reflectance Figure. Example of defining of PoI for cloudy pixels of one convective system. White arrows show the location of corresponding PoI on VIS0.6 image at 11:30 UTC on 4 July 2004.

8. Estimation of fully cloudy pixel reflectance Convective clouds are really optically thick, even if their geometrical size is not so large. R c is simulated by Radiative Transfer Model (RTM) R c of optically thick cloud does not increase with further increase of opticall and geometrical thickness of cloud

9. Cloudy pixels diagnostic on visible channels Threshold technique for cloudy pixel diagnostic. Threshold technique for cloudy pixel diagnostic. It is relatively easy to adapt thresholds to varying meteorological conditions, viewing geometry and using external data (NWP data, RTM calculation, climatological atlas). It is relatively easy to adapt thresholds to varying meteorological conditions, viewing geometry and using external data (NWP data, RTM calculation, climatological atlas). According to the Wielicki and Parker (1992) the reflectance threshold in visible channels is the most sensitive for low (young convective) clouds identification These thresholds are calculated using RTM depending on different atmospheric states, viewing geometry and different kind of surfaces. Add TOA reflectance a pre-defined margin (to account for noise and variation of surface reflectance).

10. Cloudy pixels diagnostic on visible channels Figure. Example of cloudy pixel detection on HRVIS image using threshold method. Cloudy pixels are marked by “X”. HRVIS image at 11:30 UTC on 4 July.

11. METHODS OF CLOUD FRACTION ESTIMATION FROM SEVIRI DATA N HCFF =(R HRVIS – R clear _ HRVIS )/(R c_HRVIS - R clear _ HRVIS ) N LCFF =(R 0.6 -R clear _ 0.6 )/(R c_0.6 –R clear_0.6 ) N LCFF =(R 0.8 -R clear_0.8 )/(R c_0.8 –R clear_0.8 )

12. Rapid developing thunderstorm (4 July 2005, North Africa)

13. DESCRIPTION OF CLOUD CONVECTIVE SYSTEMS III 11:00 11:15 11:30 first detection on RDT product (I ) 11:45 I

14. 12:00 12:15 first detection on RDT product (II) II

15. ANALYSIS OF CLOUD FRACTION FIELD Retrieved cloud fraction fields, superimposed on HRVIS image at 11:15 UTC on 4 July 2004: HCFF (convective system I)

16. ANALYSIS OF CLOUD FRACTION FIELD Retrieved cloud fraction fields, superimposed on VIS0.6 image at 11:15 UTC on 4 July 2004: CCFF (convective system I)

17. ANALYSIS OF CLOUD FRACTION FIELD Retrieved cloud fraction fields, superimposed on VIS0.6 image at 11:15 UTC on 4 July 2004: LCFF (convective system I)

18. ANALYSIS OF CLOUD FRACTION FIELD Retrieved cloud fraction fields, superimposed on HRVIS image at 11:45 UTC on 4 July 2004: HCFF (convective system II)

19. ANALYSIS OF CLOUD FRACTION FIELD Retrieved cloud fraction fields, superimposed on HRVIS image at 12:00 UTC on 4 July 2004: HCFF (convective system II)

20. ANALYSIS OF CLOUD FRACTION FIELD Retrieved cloud fraction fields, superimposed on VIS0.6 image at 11:45 UTC on 4 July 2004: CCFF (convective system II)

21. ANALYSIS OF CLOUD FRACTION FIELD Retrieved cloud fraction fields, superimposed on VIS0.6 image at 12:00 UTC on 4 July 2004: CCFF (convective system II)

22. ANALYSIS OF CLOUD FRACTION FIELD Retrieved cloud fraction fields, superimposed on VIS0.6 image at 11:45 UTC on 4 July 2004: LCFF (convective system II)

23. ANALYSIS OF CLOUD FRACTION FIELD Retrieved cloud fraction fields, superimposed on VIS0.6 image at 12:00 UTC on 4 July 2004: LCFF (convective system II)

24. CONCLUSION CONCLUSION  The study shows that cloud fraction of pixels can be an additional feature for earlier detection of young convective clouds.  Cloud fraction of pixel obtained using HRVIS data shows more realistic values.  In order to achieve a robust estimate of cloud fraction (and eventually cloud top temperature) for young convective clouds, a number of points should be further investigated: a) more accurate 3-D radiative transfer for the estimation of fully cloudy reflectance; b) use of integrated water vapour content from NWP; b) use of integrated water vapour content from NWP; c) usefulness and derivation of a visible reflectance map based HRVIS data c) usefulness and derivation of a visible reflectance map based HRVIS data d) advantages of more detailed microphysical and geometrical models of young convective clouds; d) advantages of more detailed microphysical and geometrical models of young convective clouds; e) correction for co-registration shift between HRVIS and VIS data. e) correction for co-registration shift between HRVIS and VIS data.

25. : www.eumetsat.int/en/area4/saf/internet/vs/vs_report/saf-nwc-iop-mfi-sci-rp-03-v1-0.pdf