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SAFNWC/MSG Cloud type/height. Application for fog/low cloud situations

SAFNWC/MSG Cloud type/height. Application for fog/low cloud situations. 14 January 2009 Hervé LE GLEAU , Marcel DERRIEN Centre de météorologie Spatiale. Lannion Météo-France. Plan. SAFNWC context Main features of SAFNWC/MSG cloud algorithms Cma cloud mask CT cloud type

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SAFNWC/MSG Cloud type/height. Application for fog/low cloud situations

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  1. SAFNWC/MSG Cloud type/height. Application for fog/low cloud situations 14 January 2009 Hervé LE GLEAU, Marcel DERRIEN Centre de météorologie Spatiale. Lannion Météo-France

  2. Plan SAFNWC context Main features of SAFNWC/MSG cloud algorithms Cma cloud mask CT cloud type CTTH cloud top temperature and height Summary of validation results Illustration with fog/low cloud situations: (including example of automatic use for fog risk mapping) Outlook

  3. SAFNWC context -SAFNWC delivers software to process data from MSG and polar platforms (METOP/NOAA) . • 63 registered users, including 29 European NMS and 3 SAFs (OSISAF, CMSAF, LSASAF) -SAFNWC/MSG SW includes three cloud products (CMa, CT, CTTH) developed by Météo-France/Lannion -Detailed description of cloud algorithms and validation results available from www.nwcsaf.inm.org -SAFNC/MSG SW v2009 (available to users in march 2009) will be used during this presentation.

  4. CMa algorithm: first step • CMa First step: • Clouds and snow are first detected in each pixel of the image using multispectral theshold techiques : • Thresholds are computed using : • Atlas:height map • land/sea mask • Climatological maps: SST • continental visible reflectance • NWP short range forecast data (at MF, Arpege 1.5 deg used): • surface temperature, • integrated atmospheric precipitable water • Thresholds tuned to radiometer’s spectral characteristics with Radiative Transfer Models in cloud free conditions (6S,RTTOV).

  5. Illustration of night-time low cloud identification Low clouds T10.8mm – T3.9mm T8.7mm – T10.8mm

  6. Illustration of daytime low cloud identification Low clouds VIS 0.6mm T3.9mm-T10.8mm Snow VIS 1.6mm

  7. CMa algorithm: second step • CMa Second step: • (only available since version v2009 (available to users in march)) • Temporal analysis and region-growing technique are applied to detect low clouds at day-night transition and fast moving clouds: • For fast moving clouds: • detect T10.8mm changes within 15 minutes • For low clouds in day-night transition: • the areas, cloudy 1hour before, that have unchanged T10.8mm, T12.0mm and T8.7mm during last hour are said cloudy + • spatial extension of these cloudy areas to adjacent areas having similar Vis06mm reflectance and T10.8mm

  8. Illustration of improvement with temporal analysis

  9. Illustration of improvement with temporal analysis IR 10.8 mm BRF 0.6 mm + Masque nuage+ amélioration+

  10. CT algorithm • Cloudy pixels are classified according their radiative characteristics: • Semi-transparent and fractional clouds are distinguished from low/medium/high clouds using spectral features: low T10.8mm-T12.0mm, low T8.7mm-T10.8mm • high T10.8mm-T3.9mm (night), high R0.6mm (day) • Low, mid-level and high clouds are then separated by comparing their T10.8mm to combination of NWP forecast temperature at various pressure levels [850, 700, 500 hPa and at tropopause levels]. • Confusion between Low and mid-level in case strong thermal inversion is reduced by using T10.8mm-WV73mm

  11. CT example

  12. CTTH algorithm • Vertical temperature & humidity profile forecast by NWP needed (ARPEGE used to process cloud workshop selected cases) • TOA radiances from the top of overcast opaque clouds put at various pressure levels are simulated with RTTOV (NWP vertical profiles are temporally interpolated to each slot) • Cloud top pressure is first extracted using RTTOV simulated radiances; Method depending on cloud type. • Cloud top temperature & height are derived from their pressure (using vertical temperature & humidity profile forecast by NWP).

  13. CTTH algorithm For opaque clouds (known from CT) The cloud top pressure corresponds to the best fit between the simulated and measured 10.8mm radiances For semi-transparent clouds : Derived from a window channel 10.8mm and a sounding channel (13.4mm, 7.3mm or 6.2mm) For broken low clouds No technique has yet been implemented.

  14. Measured brightness temperature Retrieved cloud top pressure Illustration of opaque clouds cloud top pressure retrieval

  15. Retrieved cloud top pressure Measured brightness temperature Illustration of opaque clouds cloud top pressure retrieval in case thermal inversion

  16. CTTH pressure example

  17. Summary of CMa validation with SYNOP High FAR partly due to error in night-time human cloud observation. Lower POD mainly due to low cloud underdetection 500manned continental station over Europe from 1st Decembre 2007to23rd August 2008 Following cloudiness are compared: SEVIRI: average cloudiness in a 5x5 target SYNOP: total observed cloudiness

  18. Summary of CT visual inspection (related to low cloud) Low clouds may be occasionaly undetected at night-time (especially oceanic rather warm Sc advected above not too cold ground) Snow is not detected at night-time and may be confused with clouds Low cloud identication at day-night transition mainly solved in v2009. Stability of CT classifier to illumination, except for snow (not detected by CMa at nighttime) Over land, tendency to classify low clouds as mid-level(in case strong thermal inversion)

  19. Validation of low cloud CTTH with ground-based radar September 2003-October 2004 Following cloud top height are compared: derived from cloud radar (95Ghz) from SIRTA (LMD, near Paris) computed from SEVIRI (CTH_SEVIRI - CTH_radar > 0) = SEVIRI CTH overestimation

  20. 14/02/08: documented by Maria Putsay (Hungary) on Eumetsat web Image gallery 13/02/2008 21h – 14/02/2008 7h45

  21. 14/02/08: documented by Maria Putsay (Hungary) on Eumetsat web Image gallery 13/02/2008 21h00 – 14/02/2008 23h00

  22. Exemple of automated use for fog risk mapping • A combined use of: SAFNWC/MSG CT , rain accumulation and • NWP analysis (air humidity (2m), wind (10m))

  23. Outlook • SAFNWC/MSG version (v2009) used in this presentation will be • available to users in march 2009: • improved low cloud detection especially at low solar elevation, • improved cloud top height • -reduced bias, • -configurable grid size for histogram analysis • Following improvements will be included in SAFNWC/MSG v2010: • CMa improvement: use of HRV (Cu, valley fog) • CT improvement: • -implementation of cloud phase identification • -decrease of the confusion between low and mid-level • clouds in case thermal inversion • CTTH improvement: use of RTTOV-9 instead RTTOV-7

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