heavy floods episodes on the 11 th and 12 th november 2008 south african case study

Heavy floods episodes on the 11th and 12th November 2008

South African case study

Isaac Ngwana

Outline

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• General rainfall patterns• Main weather systems• Observation network• Case study• Model results and verification• Conclusion

Rainfall patterns in SA

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Main weather systems- responsible for flooding events

TTTTropical cyclonesCut off lowFrontal system

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Weather observation stations in South Africa

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• SURFACE OBSERVATIONS 23 climate stations 23 sea surface temperature stations 25 weather offices 35 electric _manual (aws manned) 156 automatic weather stations – unmanned 165 automatic rainfall stations 1257 rainfall (monthly; ~50 provide daily data)• UPPER AIR OBSERVATIONS• 9 Upper air stations ( Including the ISLANDS – Marion and

Gough)

Weather observation stations in South Africa

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Weather observation stations in South Africa

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RADAR NETWORK in South Africa

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Case study (11 & 12 Nov 2008) - general damage

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• 47 people were killed • Thousands evacuated • Roads and bridges swept away

Warning System-print media

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Early Warning System-print media cont…

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11 November 2008

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Observations - 11 November 2008

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Observed 24 hr rainfall 11 & 12 Nov 2012

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11 November 2008

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Observations -12 November 2008

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Weather systems – 11 Nov 2012

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Observed weather -11 Nov 2008

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Observed weather 12 Nov 2008

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Observed weather -12 Nov 2008

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NWP Modeling in South Africa

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NWP Modeling in South Africa

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Model Domain Assimilation

UM SA12 Large No data assimilation

UM SA12 Large Data assimilation

UM SA15 Small No data assimilation

WRF SA12 Large No data Assimilation

WRF SA15 Small No data assimilation

Modeling - weather systems 12 -48 hr forecast

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12 November 2008

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12 November 2008

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Model -12 November 2008

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Eye ball verification

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Warning System-SAWS website

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Conclusion

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• 11 &12 Nov 2008 flooding events were well forecast by the model

• Forecast warning were issued in time• Necessary steps were taken by government

dept. even though 47 people lost their lives• Good observation data bank, but sparse • Local data ingest might improve model

forecast