Observing System Simulation Experiment (OSSE): Testing the OOI CI in the Real World

A distributed community of ocean scientists provided OOI CI team daily adaptive guidance for a taskable satellite, a fleet of four autonomous Slocum gliders, and a multi-vehicle network of autonomous underwater vehicles. The scientists used the coupled system to study physical forcing of the fall/winter phytoplankton bloom dynamics, which is under-sampled due to the harsh field conditions that hinder traditional sampling techniques.

Efforts were coordinated through a web portal that provided an access point for the observational and model predictions. The development of the portal was simplified by the use of IOOS standard web services for gridded data (OPeNDAP with CF conventions). During this experiment, summaries were distributed daily that described both the atmospheric and oceanographic conditions. The numerical models could be assessed individually or combined as multi-model ensembles; the model performances were evaluated in real-time against the satellite, shore-based radar, and in situ glider measurements. Users could view the model and observation comparison using the web portal.

This infrastructure was used to conduct the following two tests of the CI planning and prosecution capabilities.

1. Using CI software, we remotely coordinated an underwater network of autonomous underwater vehicles (AUVs). Scientists onshore in New Jersey used ocean color satellite data to define an area of operations; this was forwarded to planners at the NASA Jet Propulsion Laboratory in California, who then emailed AUV deployment missions back to the boat teams at sea off New Jersey. Acoustic modems on the AUVs (which enabled underwater communications between vehicles) as well as ship communications via a gateway buoy provided operators with command and control capabilities. Using this communications network, information from the AUVs, including position, speed, heading, and some scientific sensor readings was published on Google Earth and distributed to a wider community of scientists in real time. The AUVs themselves were outfitted with CI software that allowed the vehicles to autonomously adapt to the environmental features measured by their scientific sensors.

2. We enabled coordinated sampling between underwater gliders and the space based Hyperion imager flying on the Earth Observing One spacecraft. The Hyperion images are typically 7.5 km (across track) by over 100km (along track), and resolve 220 spectral bands from 0.4 to 2.5 microns with a spatial resolution of 30m. This small spatial footprint makes it difficult to ensure that in situ assets are present for calibration. The Hyperion is a task-able platform, and therefore an alternative approach would be to mobilize in situ assets and simultaneously adjust the satellite swath to be coincident. During the field experiment, both observational data and multi-model forecasts were analyzed to determine the tasking location for the satellite. These coordinates were used by the EO-1 web cabapility to re-task the spacecraft. The 48 hour model forecast was then used by CI software to plan the optimal path to co-locate any gliders within the tasked EO-1 Hyperion swath. Two gliders were successfully moved to the swath; other gliders that were not capable of reaching the swath were diverted to accomplish other science missions. This represents a major technology breakthrough in simultaneously coordinating satellite and underwater assets guided by multi-model forecasts. It provided a machine-to-machine interactive loop driven by a geographically distributed group of scientists.