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SWFDP Eastern Africa- Cascading Forecast

SWFDP Eastern Africa- Cascading Forecast. By Ruben K. Barakiza Institut Geographique du Burundi( IGEBU) Meteorological Department P.O.BOX 331 BUJUMBURA BURUNDI Email: barakiza_r@yahoo.co.uk. Objective The provision of timely and effective information

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SWFDP Eastern Africa- Cascading Forecast

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  1. SWFDP Eastern Africa- Cascading Forecast By Ruben K. Barakiza Institut Geographique du Burundi( IGEBU) Meteorological Department P.O.BOX 331 BUJUMBURA BURUNDI Email: barakiza_r@yahoo.co.uk

  2. Objective • The provision of timely and effective information • through identified institutions • that allows individuals exposed to a hazard to take adequate actions to avoid or reduce their risk and prepare for effective response

  3. Extreme weather events • Currently, the SWFD focuses on the following weather extreme events: • Heavy rains • Strong winds associated with thunderstorms • Dry spells • Ocean/lake waves

  4. Target users • Department of Disaster Management and Public Safety Authority • General public, • Various socio-economic institutions impacted by weather/climate

  5. The Cascading Forecasting Process • In the framework of the general organization of the Global Data-Processing and Forecasting System (GDPFS), the SWFDP implies a co-ordinated functioning among three types of GDPFS centres. • These are: • Global NWP Centres to provide available NWP products, including in the form of probabilities; • Regional Centres to interpret information received from the global NWP centres, • run limited-area models to refine products, • liaise with the participating NMCs; • The NMCs to issue alerts, advisories, severe weather warnings; • to liaise and collaborate with Media, and Disaster Management and CivilPprotection Authorities; and • to contribute to the evaluation of the project.

  6. Cascading FCST ( cont’d) The first phase of this project commenced October 2011 and focused on: • heavy rain, • strong winds, • sea/lake waves, and • prolonged dry spells. The participating Services and Centres in the SWFDP Eastern Africa include: • NMHSs: Kenya, Burundi, Ethiopia, Rwanda, Tanzania and Uganda • Regional Centres: RSMC, KMD - Nairobi, RSMC, TMA - Dar es Salaam; and • Global Products Centres: • European Centre for Medium Range Weather Forecast( ECMWF) • Exeter (Met Office UK), • Washington (NOAA/NCEP ), and • DWD (Germany)  

  7. NWP Product Analysis • Analysis of national observational data • Regional Productsfrom the RSMC-Nairobi • Extreme weather guidance • Risk table • Cosmos model products • Lake Victoria project • Internatinal Centres • ECMWF Determistic forecasts and EPS • Rainfall model products • NOAA NWP products, including: • 10-day precipitation forecasts, • wind flow forecast, • Atmospheric Instability indices • Uk Metoffice products • EUMETSAT Products: satellite imagery, vertical atmosphere sounding

  8. CURREWNT STATUS OF OBSERVATIONAL NETWORK IN BURUNDI The current status of meteorological observational network in Burundi is as follows: • 2 synoptic stations operating 24 hours per day. • 13 main climatological stations • 125 rainfall stations • 43 hydrometric stations at main rivers such as Ruvubu, Rusizi and Malagarazi rivers from where hydrological measurements are carried out and data collected. • 5 Automatic Weather Stations (AWS) • EUMETSAT AMESD-PUMA satellite data station

  9. National Meteorological Observational Network in Burundi

  10. ECMWF Deterministic Forecast for Bujumbura, 01/04/2013

  11. ECMWF 6-hour Rainfall Model Forecast over Burundi on 01/04/2013 ( 06UTC-12UTC)

  12. NOAA 10-day Precipitation Forecast, 18-25 May 2013

  13. NOAA/GFS 850hpa Temperature, Relative humidity and Streamlines

  14. NOAA/GFSPrecipitable water, and Convective AvailablePotentialEnergy(CAPE) on 18/05/2013 00h

  15. CAPE • Convective available potential energy(CAPE),is the amount of energy a parcel of air would have if lifted a certain distance vertically through the atmosphere. • CAPE is effectively the positive buoyancy of an air parcel and is an indicator of atmospheric instability, • It is very valuable in predicting severe weather. CAPE ValuesPotential Weather 1000 - 3000 J kg-1 Deep Convection 5000 - 7000 J kg-1 Maximum Values- (extreme atm. instability)

  16. RSMC-NAIROBIRISK TABLE - Day 1: Monday 1st April, 2013

  17. RSMC-NAIROBI Risk Table-Day 2: Tuesday 2nd April, 2013

  18. EUMETSAT, 2013-04-01 IR image at 1200UTC

  19. Case study: Example of 16 April, 2013 • Risk table: Low risk of Rainfall>50mm/24h • Guidance forecast from RSMC-Nairobi showed that much of the country expected rainfall> 50mm/24h • On 15 April, 2013: A warning for heavy rains was issued. • What happened on the ground • In the Eastern parts of Burundi, in Ruyigi Province, Muriza weather station recorded 108.6mm/24h on 16/04/2013 .

  20. RISK TABLE Day 1: Monday 15th April, 2013 Issue Monday 15th April, 2013

  21. RISK TABLE DAY 2: Tuesday 16th April, 2013

  22. Guidance Forecast for Tuesday 16th April, 2013

  23. 2013 April 16th , 24h-Total rainfall over Burundi

  24. Information Dissemination and communication • The Burundi NMHS dissemites and weather information to the socio-economic sectors and to the general public through various facilities such as: • National Radio broadcating • Telephone • Internet-emails • Gaps: • Lack of television presentation, • Lack of MOU with Newspapers • Lack of allocated budget for providing weather information ( for instance, newspapers)

  25. Relationship withdisaster management and civil protection authorities and media A networking and viable communication links exist between Burundi NMHS and the Disaster Management and Civil Protection Office. • The National Platform for disaster Management has a representative from the Burundi NMHS • UNDP-funded workshops focusing on Disaster prevention and preparedness have been organized aimed at capacity building of DMCPA, Media, NMHS and other concerned institutions. • Media have been important means for disseminating information on extreme weather events.

  26. Challenges • Most extreme weather events such as hail, strong winds, etc. are associated with small scale thunderstorms, sometimes not materialized by global models. • Inadequate feedback from user community • Low level of awareness and disaster preparedness in the general public leading to low coping capacity.

  27. THE END • THANK YOU FOR LISTENNING !!!

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