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Applications of Meteosat Data for the Characterisation of Atmospheric Instability

Applications of Meteosat Data for the Characterisation of Atmospheric Instability. Marianne König EUMETSAT marianne.koenig@eumetsat.int. Europe's Meteosat Second Generation (MSG). MSG SEVIRI * : 12 Channel Instrument 3 (1) km Pixel Size Repeat Cycle 15 min

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Applications of Meteosat Data for the Characterisation of Atmospheric Instability

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  1. Applications of Meteosat Data for the Characterisation of Atmospheric Instability Marianne König EUMETSAT marianne.koenig@eumetsat.int

  2. Europe's Meteosat Second Generation (MSG) MSG SEVIRI*: 12 Channel Instrument 3 (1) km Pixel Size Repeat Cycle 15 min (5 min. in “rapid scan service”) 1 = VIS0.6 2=VIS0.8 3=NIR1.6 12=HRV 4=IR3.9 5=WV6.2 6=WV7.3 7=IR8.7 *: SEVIRI : Spinning Enhanced Visible and InfraRed Imager 8=IR9.7 9=IR10.8 10=IR12.0 11=IR13.4

  3. MSG Scans 5 Minutes RSS: Meteosat-8 15 Minutes: Meteosat-9

  4. High Resolution Visible Channel (before 1445 UTC)

  5. High Resolution Visible Channel (after 1700 UTC)

  6. Meteosat Second Generation Setup • One of the centrally at EUMETSAT derived products is the so-called GII product (GII = Global Instability Indices) • In addition, the NWC-SAF provides software to locally derive a similar product – www.nwcsaf.org • NWC-SAF: Satellite Application Facility for Nowcasting and Short-Range Forecasting as a part of the EUMETSAT distributed ground system

  7. Part I: Pre-convective Atmospheric Conditions • Instability parameters derived from satellite data: • Instability indices (e.g. Lifted Index, K-Index) are empirical indices which describe the potential for convection • (e.g. when a given index is larger/smaller than a certain threshold value) • Originally derived from radiosonde observations: • Empirical! Can vary regionally! Not the only parameter which decides whether convection will occur!

  8. GII Algorithm Overview • The GII parameters are derived from a retrieved profile of temperature and humidity, using these six MSG channels. The algorithm is a "physical" retrieval, i.e. it uses radiative transfer calculations to find a profile which best fits the observed brightness temperatures. • Such a physical retrieval is also called "optimal estimation method".

  9. GII Algorithm – Some Details • The OE method needs a "first guess" profile – a starting point for the retrieval: • This should not be too far away from the true profile (as there are many solutions for this problem) • We take the forecasted profiles from the ECMWF global forecasts, interpolated in time and space to the pixel or pixel group position

  10. GII – Some Examples Global view of Lifted Index, K-Index and Total Precipitable Water (3 x 3 pixel averages)

  11. GII- Some Examples K-Index: forecasted by ECMWF and MSG retrieval: Value added by satellite

  12. GII – Applications at the South African Weather Service (SAWS) 26. October 2006, 1100 UTC 26. October 2006, 0800 UTC Courtesy Estelle de Coning, SAWS

  13. SAWS "Verification" Activities K-Index is related to number of lightning strokes within the next 12 hours. A thus defined POD is typically 0.7 – 0.8 Courtesy Estelle de Coning, SAWS

  14. GII at SAWS: Detailed Case Study 01 Feb 2008 Courtesy Estelle de Coning, SAWS

  15. Further Developments Done at SAWS CII: Combined Instability Index – indices are combined to assign a probability of lightning occurrence within the next six hours Courtesy Estelle de Coning, SAWS

  16. Outlook: Further Convection Products • Convective Initiation (following the work of John Mecikalski (UAH) and Kristopher Bedka (NASA)) • Aim is to identify in a group of small scale cumulus clouds those which will (soon) grow into severe convective storms • Satellite-based identification should precede the detection by radar • Concept is based on IR temperature trends and channel differences and their time trends – so-called interest fields

  17. CI: From GOES to Meteosat Second Generation 19 Interest Fields; relative importance and thresholds tuned from CI cases of the COPS experiment Relevant processes: Cloud top glaciation Updraft strength Cloud depth / height of updraft With MSG, more channel combinations are possible, but these contain redundant information

  18. CI Example: 10 July 2010 Courtesy Luca Nisi, Meteo Swiss

  19. Mature Storms: Top Texture / Overshooting Tops 23 September 2009 Mediterranean Sea “Sandwich Product” – developed by Martin Setvák (CHMI)

  20. Mature Storms: Cloud Top Microphysics RGB VIS0.8 IR3.9 – IR10.8 IR10.8 Small cold ice crystals in bright yellow 27 Oct 2004, 1200 UTC

  21. Summary • Multi-spectral geostationary satellite data provide a wealth of information which help in the short-term forecasting and nowcasting of severe storms. • More background and test cases can be found on the web site of the Convection Working Group • www.convection-wg.org

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