Observation and model resolution implications to ocean prediction
Gregg Jacobs, Joseph M. D’Addezio, Hans Ngodock, Innocent Souopgui
Abstract
We address ocean modeling capability that has grown exponentially while ocean observation growth has not maintained pace, a situation leading to seemingly degraded forecast skill when model resolution is increased. Skill in predicting ocean instabilities such as mesoscale eddies requires satellite and in situ observations continually correcting numerical model conditions. Observations constrain positions of larger ocean model features, while smaller features are unconstrained. By means of an Observation System Simulation Experiment (OSSE), we show that time–space observation coverage controls the separation of constrained and unconstrained feature scales. Using 1000 independent surface drifters, we show constrained scales have deterministic prediction skill and unconstrained scales predict areas of higher expected errors. The results are shown to be consistent with ensemble forecasts. Separating constrained and unconstrained features, and using information within each appropriately, allows us to manage the present gap between observation and model resolution.