Why models have forecast problems Initialization and quality control smooth data fields, but some of the lost detail may be important. May have poor first guess Lack of data over the oceans and Mexico. Atmospheric processes are non-linear; small changes in initial conditions can lead to large forecast variations (this is the basis for ensemble forecasting). Insufficient grid spacing to resolve features. Model physics are approximations (radiation, cloud physics, convection, boundary layer, etc.) –for lower resolution models (the current operational models), convection is parameterized –for higher resolution models (models with a resolution below 5 km) the micro-physical processes are parameterized
