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Overview of a new Ocean Glider Navigation System: OceanGNS
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Ocean gliders are increasingly a platform of choice to close the gap between traditional ship-based observations and remote sensing from floats (e.g., Argo) and satellites. However, gliders move slowly and are strongly influenced by currents, reducing useful battery life, challenging mission planning, and increasing pilot workload. We describe a new cloud-based interactive tool to plan glider navigation called OceanGNS© (Ocean Glider Navigation System). OceanGNS integrates current forecasts and historical data to enable glider route–planning at varying scales. OceanGNS utilizes optimal route–planning by minimizing low current velocity constraints by applying a Dijkstra algorithm. The complexity of the resultant path is reduced using a Ramer-Douglas Pueckler model. Users can choose the weighting for historical and forecast data as well as bathymetry and time constraints. Bathymetry is considered using a cost function approach when shallow water is not desirable to find an optimal path that also lies in deeper water. Initial field tests with OceanGNS in the Gulf of St. Lawrence and the Labrador Sea show promising results, improving the glider speed to the destination 10–30%. We use these early tests to demonstrate the utility of OceanGNS to extend glider endurance. This paper provides an overview of the tool, the results from field trials, and a future outlook.
Title: Overview of a new Ocean Glider Navigation System: OceanGNS
Description:
Ocean gliders are increasingly a platform of choice to close the gap between traditional ship-based observations and remote sensing from floats (e.
g.
, Argo) and satellites.
However, gliders move slowly and are strongly influenced by currents, reducing useful battery life, challenging mission planning, and increasing pilot workload.
We describe a new cloud-based interactive tool to plan glider navigation called OceanGNS© (Ocean Glider Navigation System).
OceanGNS integrates current forecasts and historical data to enable glider route–planning at varying scales.
OceanGNS utilizes optimal route–planning by minimizing low current velocity constraints by applying a Dijkstra algorithm.
The complexity of the resultant path is reduced using a Ramer-Douglas Pueckler model.
Users can choose the weighting for historical and forecast data as well as bathymetry and time constraints.
Bathymetry is considered using a cost function approach when shallow water is not desirable to find an optimal path that also lies in deeper water.
Initial field tests with OceanGNS in the Gulf of St.
Lawrence and the Labrador Sea show promising results, improving the glider speed to the destination 10–30%.
We use these early tests to demonstrate the utility of OceanGNS to extend glider endurance.
This paper provides an overview of the tool, the results from field trials, and a future outlook.
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