“Toward a GOOS glider programme: Tools and methods”
General Assembly
Outline
• Introduction
• Components for network design
• Results
 Homogeneous Networks
Mediterranean Sea
North Atlantic-Artic Ocean
Atlantic Ocean
 Heterogeneous Networks
Glider-Mooring
Glider-Satellite
Towards a ARGO-glider network component
• Conclusion
Introduction
GOOS is designed to:
•Monitor, understand and predict weather and climate
•Describe and forecast the state of the ocean,
including living resources
•Improve management of marine and coastal
ecosystems and resources
•Mitigate damage from natural hazards and pollution
•Protect life and property on coasts and at sea
•Enable scientific research
GOOS is made of many observation
platforms:
•3000 Argo floats
•1250 drifting buoys
•350 embarked systems on
commercial or cruising yachts.
•100 research vessels.
•200 marigraphs and holographs.
•50 commercial ships which launch
probes.
•200 moorings in open sea.
•Gliders?
Glynn Gorick’s artwork depicting the GOOS observation network through its
instrumentation and interconnections
http://www.ioc-goos.org/
Introduction
GOOS involves a heterogeneous ocean observing network involving static
nodes (moored profilers, bottom mounted systems), nodes with
uncontrolled motion (drifter buoys and profiling floats) and nodes with
controlled motion (ships). This in situ observing system is complemented
by remote sensing platforms/sensors (including acoustic, aerial or space
based).
Cooperation in a ocean observing network a performance gain in
marine sampling over naïve collective behavior
Nodes with controlled motion allow to substantially increase
cooperation (coordination). Difficult to realize by traditional platforms
of oceanographic sampling due to their physical, economic and/or
operational limits. Gliders would increase cooperation levels in GOOS.
Multi-Robotic System Taxonomy
Exploiting the observing capabilities of the different nodes must be
found by designing optimum sampling strategies to allow an accurate
representation of oceanographic processes (optimal sampling). These
sampling strategies could adapt to the evolution of the environment,
and consider possible limitations due to the motion of part of the
platforms in the network (adaptive sampling).
Components for network design
Experimental Design
Exploratory Design
Optimum Design
Base the design on a
geometric criterion that not
involves a priori knowledge
of the environment. Space
filling designs, try to spread
sampling locations
throughout the region,
leaving as few holes as
possible.
Base the design on a prior
knowledge of the
environment.
Models
-Environment
-Platform
Cost
Optimization
Components for network design
Environment
The sampled field is interpreted as a weakly stationary or second-order
stationary process defined by known background field
and
 




covariance function CM x, x'   x   x  x'  x' ( 
stands for ensemble average). CM is computed from ensembles or
spatiotemporal series of model outputs or observations.
Trajectories
Number of gliders
Initial Position
Speed
Total mission time
Time between waypoints
Surface time
b
a
g
Currents
Components for network design
Cost Function
 k1
P k   e
  o b s H k T o1b s o b s H k  k 
k
 k2
H  N 
 obs
4

 i x    N ki ki
 obs   i 
i 1
 k4
T CM1  k  
Field values at the grid nodes,
Observation matrix,
Observation error matrix
Vector of observations
 k3
A- Optimal Design
Arg min(Trace (CM  CM H T HC M H T   obs


1
HC M ))
Components for network design
Optimization
Pattern Search
Genetic Algorithm
Generate Population
Generate Mesh
Simulated Annealing
Random Perturbation
Evaluation and Selection
Compute change in cost function
Poll
Better?
Reproduction
Compute probability of acceptance
Yes
Mutation
8
Glider Ports
Results: Networks of gliders
Network Configuration
12 days mission
3 hours optimization for each month
2 days TW
Glider speed 0.35 m/s
Background statistics has been built on the basis of a time series
from 1999 to 2011 of monthly reanalysis of the temperature fields
at 50 m depth resulted form the Mediterranean Forecasting System (MFS).
Numerical covariance estimated with a shinkrage approach
Prior uncertainty- September
Optimum glider tracks
and posterior uncertainty-September
Spatially average prior (black)
and a posterior (blue) uncertainties
Glider Ports
Results: Networks of gliders
Network Configuration
12 days mission
3 hours optimization for each month
2 days TW
Glider speed 0.35 m/s
Background statistics has been built on the basis of a time series
from 1999 to 2011 of monthly reanalysis of the temperature fields
at 50 m depth resulted form the Mediterranean Forecasting System (MFS).
Numerical covariance estimated with a shinkrage approach
Prior uncertainty- September
Optimum glider tracks
and posterior uncertainty-September
Spatially average prior (black)
and a posterior (blue) uncertainties
Glider Ports
Results: Networks of gliders
Network Configuration
12 days mission
3 hours optimization for each month
2 days TW
Glider speed 0.35 m/s
Background statistics has been built on the basis of a time series
from 2003 to 2008 of monthly reanalysis of the temperature fields
at 50 m depth resulted from the Topaz ocean prediction system .
Numerical covariance estimated with a shinkrage approach
Prior uncertainty- October
Optimum glider tracks
and posterior uncertainty-October
Spatially average prior (black)
and a posterior (blue) uncertainties
Results: Networks of gliders
Network Configuration
30 days mission
Background statistics has been
built on the basis of a time series
from 20 years of monthly
reanalysis of the temperature
fields at 50 m depth resulted from
Topaz ocean prediction system
Numerical covariance estimated
with a shinkrage approach
Results: Networks of gliders
Network Configuration
10 days mission
Background statistics has been
built on the basis of a time series
from 20 years of monthly
reanalysis of the temperature
fields at 50 m depth resulted from
Topaz ocean prediction system
Numerical covariance estimated
with a shinkrage approach
Results: Networks of glider-moorings
Optimum glider trajectory to get near-optimal temperature estimations in the first
200 m of the water column. The region selected was a rectangle of approximately
60 x 60 Km2 in the Ligurian Sea centered on the ODAS Italia1W1M3A Eulerian observatory . Optimum mission designs for a glider were computed for the
described area for August 20th -24th.
A 30-day integration during August 2010 of the operational Navy Coastal Ocean Model
(NCOM, Martin, 2010) is considered in this study. NCOM was coupled to the
Navy Coupled Ocean Data Assimilation (NCODA) system.
The time-evolving thermal field resulting from the 30-day simulation
is considered as the ‘‘truth’’ from which glider and mooring data are simulated.
Truth temperature field
August 20th -24th.
Inferred temperature field
August 20th -24th.
Uncertainty of temperature field
August 20th -24th.
Results: Networks of glider-satellite
Field Experiment
Glider + Satellite data
Glider data
Glider data
Satellite data
Glider data ----Glider + satellite
Results: Towards a global Argo-Glider network component
Conclusion
Observational oceanography is transitioning from platform based capabilities to networks of sensor
and platforms
Cooperation and coordination are fundamental aspects of the networking paradigm
Platforms which motion is controlled (eg. gliders) play a relevant role to implement cooperation in
networks
Gliders bring new scientific and technological demands into observational oceanography.
Exploiting synergism between different ocean observing platforms
Integrating the observations gathered by different platforms into a unique picture
GROOM has developed models and methodologies to attempt to satisfy the above demand
It is envisioned that the design of a ARGO-glider network in GOOS would be very significant