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