Objective:
The DEMEN project will develop, evaluate, and apply ensemble and super-ensemble techniques to satellite ocean color imagery, to improve atmospheric correction and bio-optical inversion algorithms. We will extend advances by the numerical modeling community to represent observational and algorithm error sources for ocean ensembles, adn we will evaluate the ensemble representations and assess ensemble metrics. The ensemble approach will be applied at several levels, on the initial satellite data (reflectances), on the derived optical properties (partitioned absorption coefficients, backscattering coefficients, chlorophyll concentration, suspended particulates, diffuse attenuation, and euphotic depth), and on the second-order optical products derived for Navy applications (diver visibility, laser penetration, probability of target detection/identification). We will perform quantitative error evaluations and error cascading on the end-to-end processing, to estimate uncertainties in satellite-derived optical properties. Rather than providing only "best guess" optical products to end-users as has been done in the past, this approach will additionally provide important new information in the form of error maps and will optimize the accuracy and value of the optical properties, forecasts, and sensor performance surfaces.
