1. system goal
platform
controller
data
model

2. 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

3. asap pilot #1: march 2006

4. asap pilot #2: may 2006

5. asap and loco profile locations

7. 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

8. 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?