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Effective Use of Satellite Information in Severe Wx Fcasting : Use of blended data products:

J.N. Kagenyi Senior Meteorologist RS/GIS IMTR-NAIROBI SWFDP NOV.2012. Effective Use of Satellite Information in Severe Wx Fcasting : Use of blended data products:. Data available. Synoptic (MDD)

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Effective Use of Satellite Information in Severe Wx Fcasting : Use of blended data products:

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  1. J.N. Kagenyi Senior Meteorologist RS/GIS IMTR-NAIROBI SWFDPNOV.2012 Effective Use of Satellite Information in Severe WxFcasting:Use of blended data products:

  2. Data available • Synoptic (MDD) • Satellite (MSG Channels, RGB products) MPEF, SAF Products e.g. (Now casting SAF), MODIS products, jason etc • NWP models • different spatial and temporal resolutions (UK Models (3,to 0.5 deg), WAFs,ECMWF models, Local models (LAM, regional models) and many more • Diagnostic models (manual plots and analyses, development and forecast) • Radar data for nowcasting, • Climatology (seasonal information to help with shift from normal)

  3. Synergie System designed to: Integration of all data • Integrate all data in a workstation • Equipping the weather person with power of information • Role for experienced Forecaster is to obtain optimum information from ALL the available data to make Reliable Forecast • Capacity to handle all this data is the subject of your presence Here!

  4. Possible Steps in Severe WX Identification Using Synergie system • Assumptions • all participants have access of Synergie system back home. • Availability of Near Real Time (NRT) data for this exercise. • Step 1. What season are we? Rainy season! Hence certainty of S/WX case is more certain • Step 2. display a VIS Image if daytime otherwise ch4 (3.9) or RGB Convection(10-9,9-4,9-0) at night time, to identify location of any convection by water clouds

  5. Step 2: Display VIS (day) Several convective cells at different stages of development

  6. Step3: Use WV5 and WV6 • Use WV 6.2 and 7.3 microns to identify potential areas with enough moisture to support further development (Deep column) • White areas are moist and cold, dark or grey areas are low in WV and warmer. • Upper level moisture from WV5 (6.2) • Medium Level moisture from WV6 (7.3)

  7. Display WV5 data for Moisture Convective cells are within the high density water vapour. They exist in convergence zone.region Moisture supply is high Time is 12:12 pm Further development possible likely

  8. Display WV-6 for Mid Level Moisture Sufficient moisture at mid levels Question Is there enough Thermodynamic (Pressure Field) convergence at lower levels and Upper air diffluence (wind data or Animations) to generate deep convection?

  9. Step 4: Animate to Identify trend on convective areas • You may animate Vis or IR, RGB consecutive images for the last 15,30,45,60 minutes • This will answer the two questions: • Is convection increasing or decaying? • What direction are systems leading? • How fast is the system developing? • Channel 3(1.6): good for daytime as reflectance reduces for developing severe storms and brighteness for decaying storms • At night use night time microphysics RGB (10-9,9-4,9-0) or 5-6,9-4,5i air-mass)

  10. Animation on Dev. convection • Animate=2003 observation. • It is evidence on this animation that between 8-12:30 • the region around southern lake Victorian and land east of the lake developed from shallow convection, white reflectance on ch3 to deep convection of large ice crystals (low reflectance=grey) similarly over Kisumu and Western Nyanza of Kenya. • use Ch9 to confirms temp changes.

  11. Step 5: Confirm microphysics or stages of convection by RGB149, • Deep red indicates very cold large ice clouds. • The Yellow brown are areas with small ice and water clouds at stage of development see over lake Tanganyika. • Cyan is cloud free and warm • Deep Blue is warm water body

  12. Step 5:Confirm microphysics or stages of convection by RGB139 • Deep dirty brown indicates very cold large ice clouds. • The yellow areas are clouds with small ice and water clouds at stage of development- Super-cooled (-33 deg) clouds, see over lake Tanganyika. • Cyan is cloud free and warm • Deep Blue is warm water body. • Thin Cirrus clouds appear dark brown

  13. Step 6: Get synoptic data 3-6 hours • View reported synoptic data for synoptic hours. • Confirm from dry bulb and web bulb data for drying or increase in moisture from depression values or differences of ( Ta-Td) • You may use conditional plotting of Synergy tools. • Area whose( Ta-Td) <=1 Moist or cloudy • Areas whose Ta-Td =>8 dry air conditions

  14. Full Synoptic chart at 15:00 utc Scarce data stations requires to be complemented with Satellite data

  15. Overlay Display: Airmassrgb , Synoptic chart Surface obs on-22/11/11 Current Wind analysis suggests a convergence zone as shown Position of deep convection is supported by convergence of the winds Moist Easterly winds supply moisture

  16. 2/2 Synoptic chart: with Overlay AirmassSA -22/11/11 Dry cold air-mass from Southern Tip of Africa. This appears to be under St Hellena high pressure ridge cell (descending air). supports synoptic p/gradient Moisture: dry bulb 30oC wet bulb -4oC. (Dry air)

  17. Step 7. Severe weather arises from deep convection • Deep convection goes beyond medium and upper tropopause hence • Any weather feature that causes WV5-WV6 >0 generates TS and Heavy Showers/ rain • Solar radiation & Mechanical lifting (wind field convergence zone or orographic lifting) contribute to development of severe weather.

  18. Step 7: analysis WV5-WV6 product Dark is negative White is positive or almost equal to zero Analysis for Deep convection

  19. Step8: WV5 Merge with AIRMASS RGB Synergie Under tool important for And comparison WV5 under Airmass RGB

  20. Areas of deep convection RGb139 Areas of deep convection in Red

  21. Step 9: AIRMASS RGB (5-6,8-9,0-5) To detect dry and moist air sources

  22. Step 10: WV5 and areas of Jetstreams Positions of Jetstreams affects Weather in their region of influence for instance creation of strong outflows at the tropopause level . The Onset of a season could be associated with the intensity and the position of Jetstreams E g. EAST Africa Low Level jet (July –September)

  23. Step 11: ECMF2.5 ITCZ and Areas of confluence on 850 hPa wind :ea Satellite imagery confirms the identification of convergence zone by a belt of Cloud clusters. Image analysis gives colours that depict the type of system and the stage of development

  24. Step12: NWP wind at 850 Hpa 22.11.11 • Areas of low level convergence • Corresponds with areas of deep clouds • Causing High MPE values

  25. Data: Merging three layers Streamlines on 200 hPaBLUE 850 hPa (RED) and IR 10.8 on background .24 hrs shift. Notice the position of Outflow on 200

  26. Compare RGB and NWP Wind

  27. NWP AND SW FCASTING NWP data and the Synoptic data report. Gives great harmony on data analysis to forecasters. and In particular when NWP replicates the weather as observed on Satellite The Belt of convergence on NWP wind analysis on 850 hPa identifies areas of expected Severe Weather. Meridional Arm of ITCZ is denoted red The Identification of pressure features provides tools of decision making in Wxfcasting.

  28. 22/11/11 MPE Rain estimate Will your forecast give better Results than this estimate

  29. MPE Overlay on day microphysics RGB

  30. WV6 mid level moisture: 22/11/11 Active ITCZ zone is clearly defined on this mid level signal map. DRC and neighboring countries, Botswana Zambia, are wet. Seasonally the Sothern African countries Except Namibia and SA are experiencing the Onset of rains.

  31. Upper Air data for Nowcasting Ascent analysis: Very Unstable at lower levels. Generally moist atmosphere but dry at 450 hpa Easterly Wind pattern conducive for afternoon WX activities Fcast: Showers and T/S over several places over Nairobi and Highlands East . Lake basin: Heavy TS/Showers over most places. Flooding Alert! In Lake region

  32. Presentation of WxFcast for your Area • Exercise: Create a forecast for Severe WX for your regions: 5 slides • Present to us. • Use Synergie • Form groups of 4 • Use 20 Minutes:

  33. Goodbye • Kwaherina ASANTE

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