aim high with the goal in mind. exp 2 exp 3 exp 1largest response:
TRANSCRIPT
LRF Modelling2008/09
Physical Models Empirical Models
AGCM(ECHAM4.5 – for GPC recognition)
Asmerom BerakiCobus Olivier
NEC
CGCM(Feasibility study ECHAM-MOM:
Implement on SA machine)Asmerom Beraki
CHPC
Application Model(crops)
Willem LandmanNoelien Somers (ARC)
Global SST scenariosWillem LandmanPC & LORENZ
MM Ensemble for LRF & CCWillem LandmanPC & LORENZ
Application Models(streamflow and malaria)
Willem LandmanPC & LORENZ
Verification
Models(ECHAM4.5+GPC+Statistical)
Asmerom BerakiCobus Olivier
Willem Landman
Operational ForecastsCobus Olivier
External Model Data(HadGEM, GloSea4 and GPCs)
Cobus OlivierLORENZ
SADC-DMC products (rainfall and temperatures)
Willem LandmanPC & LORENZ
Aim high with the goal in mind
Exp 2Exp 3
Exp 1Largest
response:
• AGCM better AGCM better able to capture able to capture trendtrend• Here, AGCM (in Here, AGCM (in forecast mode) is forecast mode) is therefore a therefore a better better representation of representation of realityreality• Should thus Should thus give better give better predictions of predictions of rainfall over rainfall over South Africa than South Africa than CGCMCGCM
NCEP vs AGCM = 0.4581NCEP vs CGCM = 0.3775
• Model output statistics (MOS) applied to
• AGCM ensemble mean SLP
• CGCM ensemble mean SLP
• Verification
• 5-year-out cross-validation
•Spearman rank correlation
AGCM-MOSslp – CGCM-MOSslp
Only about 5% of the stations show local significant correlation differences at the 95% level
Forecast skill not significantly different irrespective of the use of “correct” or “incorrect” SLP forecasts