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PhD Candidate Trygve Olav Fossum
Data-Driven SamplingWhat Robots Can Do For Ocean Science
Trygve Olav Fossum
Department of Marine Technology
Norwegian University of Science and Technology
http://ntnu.edu/employees/trygve.o.fossum
MotivationTraditional practice:
PhD Candidate Trygve Olav Fossum
Sampling the upper ocean
Remote Sensing
Drifters / Profiling floats
Fixed Moorings (time series stations)
Ship based and USV
Propelled and Glider AUVs
Coastal Networks
UAV and balloons
The tools to study the upper ocean
PhD Candidate Trygve Olav Fossum
Ocean Models
B
U
O
Y
S
Ship
based
Remote Sensing
Gilder AUVs
Propelled AUVs
Ocean
sampling
Experiment design should be driven by
models and remote sensing, and follow
the value of information concept. The data
should be collected using autonomous
resources, supplemented with data
traditional in-situ assets.
Ocean sampling is dependent on a range
of sources to “fill the gaps”
FLOATERS
PhD Candidate Trygve Olav Fossum
AUVsInstruments and measurements from
AUVs (Autonomous Underwater
Vehicles).
PhD Candidate Trygve Olav Fossum
Data-Driven
sampling
Combine ocean models with robotic- and
remote sensing in order to render an
accurate representation of the ocean.
Use data-driven sampling to strategize
sampling efforts.
PhD Candidate Trygve Olav Fossum
Adaptive vs. Non-adaptive
PhD Candidate Trygve Olav Fossum
Sense Act
Sense Model Plan Act
Adaptive / Data-Driven Approach:
Sense Model Plan Act
Field
campaignResults from ENTiCE campaign. The
project set out to map and understand the
productive Froan archipelago.
Ocean Models
B
U
O
Y
S
Ship
based
Remote Sensing
Gilder AUVs
Propelled AUVs
PhD Candidate Trygve Olav Fossum
Temperature
dynamicsTemperature is related to a number of
ocean phenomena and events; upwelling,
frontal zones, internal waves, local
circulation and turbulence, eddies and
biomass accumulation.
Idea: Use temperature variation, as
predicted by model, as a indicator of
dynamically active regions and as a way to
understand and approach model deviation
and shortcomings.
Surface temperature from SINMOD (ocean model) at
the Froan archipelago (Mid – Norway)
The date:
PhD Candidate Trygve Olav Fossum
Internal
dynamicsTemperature is related to a number of
ocean phenomena and events; upwelling,
frontal zones, internal waves, local
circulation and turbulence, eddies and
biomass accumulation.
Idea: Use temperature variation, as
predicted by model, as a indicator of
dynamically active regions and as a way to
understand and approach model deviation
and shortcomings.
3D temperature structures and internal dynamics
Salinity layers (Isopycnals)
PhD Candidate Trygve Olav Fossum
Finding a
suitable
survey areaAnalyze the dynamics in the model and
calculate the most interesting area.
A map of the empirical temperature variance
PhD Candidate Trygve Olav Fossum
Data-Driven
sampling
Combine ocean models with robotic- and
remote sensing in order to render an
accurate representation of the ocean.
Use data-driven sampling to strategize
sampling efforts.
PhD Candidate Trygve Olav Fossum
Information driven planning with AUV: Diving into ocean ecosystems – Trygve Fossum NTNU 2016
Data interpretation
Spatial reconstruction based on:
1-Gaussian Fields
2-“kriging” – spatial interpolationPhD Candidate Trygve Olav Fossum
Assimilation
in proxy modelFormulate a surrogate ocean model based
on a Gaussian Process and assimilate
online towards this proxy model.
PhD Candidate Trygve Olav Fossum
Algorithm and
WaypointsUse an objective function to find locations
that have high variance and gradients.
PhD Candidate Trygve Olav Fossum
Adaptive
behaviorResults from ENTiCE campaign. The
project set out to map and understand the
productive Froan archipelago.
PhD Candidate Trygve Olav Fossum
Comparing
with OMDComparing SINMOD (ocean model) data
with in-situ measurements across different
depths and distance.
PhD Candidate Trygve Olav Fossum
ConclusionThe ENTiCE project is one step in the right direction
– Combines robotic sampling and ocean models.
Ocean models as the starting point for ocean
sampling.
More have to be done towards assimilation of data
into the high fidelity model (SINMOD).
The first step is to adjust parameters and work
scenario based.
AUVs can efficiently find and provide the in-situ data.
Has to be accompanied by data from other sources
(remote sensing, buoy, ships, etc.)
PhD Candidate Trygve Olav Fossum
AcknowledgementsThe entire ENTiCE project team.
Special thanks to: Jo Eidsvik, Ingrid Ellingsen, Morten
Alver, Geir Johnsen, Martin Ludvigsen and Kanna Rajan.
ENTiCE is funded by the Research Council of Norway.
PhD Candidate Trygve Olav Fossum