system goal platform controller data model

research goals describe upwelling plume with sufficient measurement density and spatial distribution to facilitate model validation and data denial / optimal asset distribution experiments observe and quantify hydrographic structures on scales approximating model grid resolution, both phenomenologically and statistically refine real-time data quality assessment procedures and standardize across measurement platforms explore ways in which novel adaptive sampling approaches can co-exist with more traditional observing methodologies, both with and without reference to an assimilating numerical model

asap pilot #1: march 2006

asap pilot #2: may 2006

asap and loco profile locations

summary of asap deployment 6 gliders: 136 glider-days 3270 km trackline 10619 total profiles (973 low-res real-time) overall vehicle and network performance good. coordination with PU excellent: automated control worked very well plagued by several slow leaks and one catastrophic buoyancy pump failure performance in high current / shallow water as expected data quality acceptable

things to do today/tomorrow: agree upon terms to describe different types of adaptation - environmental - operational - array geometry - changes in scientific objectives / approach / assumptions - that-thing-we-talk-about-doing-relative-to-model-forecast-error define ground-rules for model data comparison - which observations are available yet unassimilated? - advantages of evaluating “best” simulation vs. real-time product generate prioritized list of near-term publications and talks/posters how can our experience/insight support/inform navy objectives/priorities? are we on the right track? can we prove it?