Ocean Data Assimilation Guidance Using Uncertainty Forecasts

摘要

This paper discusses preliminary tests on using predicted forecast errors to estimate the impact of observations in correcting the Naval Research Laboratory (NRI.) tide resolving, high resolution regional version of the Navy Coastal Ocean Model (RNCOM) assimilating local observations processed through the NRI. Coupled Ocean Data Assimilation (NCODA) system. Since there will always be a shortfall of data to constraint all sources of uncertainty there is an obvious advantage to optimally guide observations to reduce model errors that could be producing the most negative impacts. The importance of this topic has been further heightened in oceanic applications by the advent of Underwater Automated Vehicles (UAVs) that can bring persistent observations but need to be told where to go and when, following regular schedules. This works tests a technique named the Ensemble Transform Kalman Filter (ETKF) that can be used to automate such adaptive sampling guidance and has been successfully applied for atmospheric modeling optimization. The ETKF uses an ensemble of state-fields from a certain initialization time and rapid low rank solutions of the Kalman filter equations to estimate integrated predicted error reduction for selected target ensemble variables, or combinations of variables, over areas and forecast ranges of interest. The error estimates are produced through independent RNCOM runs using perturbed forcing and initial conditions constrained at each analysis time by new estimates of the analysis errors as provided by NCODA, using a technique named Ensemble Transform (ET).