Physical Sciences and Mathematics Commons^™

Hydrographic Data Processing On A Robust, Network-Coupled Parallel Cluster, Rohit Venugopal, Brian R. Calder

Center for Coastal and Ocean Mapping

Increasing data volumes and adoption of computer-assisted hydrographic data processing algorithms necessitate higher data processing rates if gains in efficiency achieved in the last decade are to be maintained and enhanced. Recent advances in desktop computer architectures have made multi-core and multi-processor systems readily available, and some advances have been made in implementing multi-threaded versions of common hydrographic data processing algorithms. In many cases, however, although the algorithms might be ideal for parallel implementation (so called ‘embarrassingly parallel’ tasks), limitations in memory, disc and network bandwidth within a single system can have significant limitations on the scalability of these solutions. …

Use (And Potential Abuse) Of Uncertainty In Hydrography, Brian R. Calder

Center for Coastal and Ocean Mapping

The evaluation and use of uncertainty as a component of hydrographic data processing systems has grown considerably in the last decade. Uncertainty models for sounding data are now common, and progress has been made in developing models, methods and implementations for preserving this uncertainty in intermediate hydrographic data products. Less progress has been made in dealing with expressing the uncertainty in hydrographic data products to the user, however, which we contend should be our ultimate aim.

We draw here a distinction between the uncertainty assessed for observed sounding (and auxiliary) data and uncertainty as expressed to the user, and observe …