ASSIMILATION OF A KNOWLEDGE BASE AND PHYSICAL MODELS OF SEA ICE TO REDUCE ERRORS IN PASSIVE-MICROWAVE CLASSIFICATIONS

An expest system framework has been developed to classify sea ice types using satellite passive microwave data, an operational classification algorithm, spatial and temporal information, ice types estimated from a dynamic-thermodynamic model, output from a neural network that detects the onset of melt, and knowledge about season and region. The rule base imposes boundary conditions ubn the ice classification, modifies parameters in the ice algorithm, determines a "confidence" measure for the classified data, and under certain conditions, replaces the algorithm output with model output. Results demonstrate the potential power of such a system for minimizing overall error in the classification and for providing nonexpert data users with some way of assessing the usefulness of the classification results for their applications.