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Drs. Matayo Indeje, L. Sun, J. Mutemi & L.J. Ogallo

Documenting Results of Dynamical Downscaling of Climate Forecasts over the Equatorial East Africa Using Regional Spectral Model. Drs. Matayo Indeje, L. Sun, J. Mutemi & L.J. Ogallo. 11 th international RSM workshop, August15-19, 2011 , National Central University, Jongli, Taiwan.

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Drs. Matayo Indeje, L. Sun, J. Mutemi & L.J. Ogallo

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  1. Documenting Results of Dynamical Downscaling of Climate Forecasts over the Equatorial East Africa Using Regional Spectral Model Drs. Matayo Indeje, L. Sun, J. Mutemi & L.J. Ogallo 11th international RSM workshop, August15-19, 2011, National Central University, Jongli, Taiwan

  2. STUDY AREA Rainfall Annual Cycle Blue – Eastern half of the region Red – western half of the region Equatorial Eastern Africa

  3. Reason for downscaling Displacement of Orography in Global Models GCM OROGRAPHY ON A T42 GRID RSM OROGRAPHY ON A 55KM GRID

  4. Regional Climate Model Challenges Over Eastern Africa Trukana Channel Ethiopian Highlands Ruwenzoris East African Highlands Indian Ocean Congo Forest Lake Victoria • Complex Topographic features > Land/sea, Land/Lake contrasts • Orographic Forcing > East Africa Highlands, Ethiopian Highlands, Ruwenzoris, Turkana Channeling Effect • Diverse Vegetation Types

  5. NCEP REGIONAL SPECTRAL MODEL (RSM-CVS)

  6. 30-YEAR MODEL CLIMATOLOGY RSM GCM OBSERVATION

  7. Dynamical downscaling: Nesting a high resolution dynamical model within a global GCM. OND 1997 OND 1998 OND 1999 AGCM (250km res.) Regional Spectral Model (55km res.)

  8. RSM CASE STUDY FOR ANOMALOUS YEARS (El Nino/wet 1997 and La Nina/Dry 1999) Dry 1999 Difference Wet 1997

  9. Simulation of Regional Circulation Patterns at 850 and 200-hPa over GHA

  10. DAILY RAINFALL REALIZATION: RSM Vs OBSERVATIONS

  11. MODEL VALIDATION

  12. EOF Analysis: RSM Vs Observation Corr. Coef = 0.78 50 % Variance 46 % Variance

  13. EXAMPLE OF REAL TIME SEASONAL DYNAMICAL FORECAST: OCTOBER TO DECEMBER 2004 AN NN BN 55 KM RESOLUTION

  14. Regional Climate Model Products are Tailored for Application in; • Crop Modeling (Agriculture and Food Security) • Disease Monitoring (Malaria, RVF, etc) • Hydrological Applications (hydro-power)

  15. CLIMATE RELATED RISK MANAGEMENT . HUNGER - DROUGHT – RAINFALL DEFICIT. DISEASE – RAINFALL AND TEMPERATURE

  16. MalariaEpidemic Prediction Model For East Africa .Epidemic malaria in the highlands(Altitude: 1500-2500 meters above sea level) .Malaria cases increased Threfold in the regionsince 1990

  17. The model uses climate data to forecast an epidemic risk Where ER is the epidemic risk Ti is the current mean monthly maximum temperature anomaly Ri is the current mean monthly rainfall above 150 mm threshold for Tm is the maximum intensity index for monthly mean temperature anomaly (Climatology) Rm is the maximum intensity index for monthly mean rainfall anomaly (Climatology) Rainfall above 300 mm per month takes on negative index values as such rainfall causes flashing of larvae thus reducing transmission. Epidemic Risk (ER) above 50% indicates a high risk of an epidemic.

  18. Research on Climate Change Downscaled Scenario • Physical and Dynamical Mechanisms responsible for the projected trend in rainfall and Temperature

  19. Thank you

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