“Toward a GOOS glider programme: Tools and methods”General Assembly

Outline

• Introduction• Components for network design• Results

Homogeneous NetworksMediterranean SeaNorth Atlantic-Artic OceanAtlantic Ocean

Heterogeneous NetworksGlider-MooringGlider-SatelliteTowards a ARGO-glider network component

• Conclusion

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/

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

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).

Multi-Robotic System Taxonomy

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.

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).

Introduction

Components for network design

Experimental Design

Exploratory Design Optimum DesignBase 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

Trajectories

Number of glidersInitial PositionSpeedTotal mission timeTime between waypointsSurface time

L=Tw

*V

L=Tw

*(V+

Vc)

Currents

Environment The sampled field is interpreted as a weakly stationary or second-order stationary process defined by known background field and covariance function ( stands for ensemble average). CM is computed from ensembles or spatiotemporal series of model outputs or observations.

''', xxxxxxCM

1k 2k

3k4k

kii

kii Nx

4

1

kMT

kkobsobsT

kobs CHHk eP

11

k Field values at the grid nodes,

Observation matrix, NH

obs Observation error matrix

iobs Vector of observations

))(min(1

MobsT

MT

MM HCHHCHCCTraceArg

A- Optimal Design

Cost Function

Components for network design

8

Optimization Pattern Search Genetic Algorithm Simulated Annealing

Generate Mesh

PollBetter?

Yes

Generate Population

Evaluation and Selection

Reproduction

Mutation

Random Perturbation

Compute change in cost function

Compute probability of acceptance

Components for network design

Results: Networks of gliders

Network Configuration12 days mission3 hours optimization for each month2 days TWGlider speed 0.35 m/sBackground 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

Glider Ports

Prior uncertainty- SeptemberOptimum glider tracksand posterior uncertainty-September

Spatially average prior (black)and a posterior (blue) uncertainties

Results: Networks of gliders

Network Configuration12 days mission3 hours optimization for each month2 days TWGlider speed 0.35 m/sBackground 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

Glider Ports

Prior uncertainty- SeptemberOptimum glider tracksand posterior uncertainty-September

Spatially average prior (black)and a posterior (blue) uncertainties

Results: Networks of gliders

Network Configuration12 days mission3 hours optimization for each month2 days TWGlider speed 0.35 m/sBackground 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

Glider Ports

Prior uncertainty- OctoberOptimum glider tracksand posterior uncertainty-October

Spatially average prior (black)and a posterior (blue) uncertainties

Results: Networks of gliders

Network Configuration30 days missionBackground 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 systemNumerical covariance estimated with a shinkrage approach

Results: Networks of gliders

Network Configuration10 days missionBackground 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 systemNumerical covariance estimated with a shinkrage approach

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 Italia1- W1M3A 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.

Inferred temperature fieldAugust 20th -24th.

Truth temperature fieldAugust 20th -24th.

Uncertainty of temperature fieldAugust 20th -24th.

Results: Networks of glider-moorings

Results: Networks of glider-satellite Field Experiment Glider data Satellite data

Glider + Satellite data Glider data Glider data -----Glider + satellite

Results: Towards a global Argo-Glider network component

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 platformsIntegrating 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

Conclusion