Progress toward autonomous ocean sampling networks

The goals of the Autonomous Ocean Sampling Network (AOSN) are reviewed and progress toward those goals is assessed based on results of recent, major field experiments. Major milestones include the automated control of multiple, mobile sensors for weeks using spatial coverage metrics and the transition from engineering a reliable data stream to managing the complexities of decision-making based on the data and the possibilities of timely feedback.

[1]  Pierre F. J. Lermusiaux,et al.  Adaptive modeling, adaptive data assimilation and adaptive sampling , 2007 .

[2]  Mark A. Moline,et al.  Bioluminescence to reveal structure and interaction of coastal planktonic communities , 2009 .

[3]  Francisco P. Chavez,et al.  Thin phytoplankton layer formation at eddies, filaments, and fronts in a coastal upwelling zone , 2009 .

[4]  J. Bellingham,et al.  Autonomous Oceanographic Sampling Networks , 1993 .

[5]  Roberto Buizza,et al.  Forecast skill of the ECMWF model using targeted observations during FASTEX , 1999 .

[6]  Naomi Ehrich Leonard,et al.  Control of coordinated patterns for ocean sampling , 2007, Int. J. Control.

[7]  Ronald Gelaro,et al.  A Predictability Study Using Geostationary Satellite Wind Observations during NORPEX , 2000 .

[8]  James C. McWilliams,et al.  Modeling tides in Monterey Bay, California , 2009 .

[9]  David M. Fratantoni,et al.  Development, implementation and evaluation of a data-assimilative ocean forecasting system off the central California coast , 2009 .

[10]  Igor Shulman,et al.  Methodology for a regional tidal model evaluation, with application to central California , 2009 .

[11]  James G. Bellingham.  Autonomous Ocean Sampling Networks , 2006 .

[12]  Craig H. Bishop,et al.  Adaptive sampling with the ensemble transform Kalman filter , 2001 .

[13]  K. Rajan,et al.  A Collaborative Portal for Ocean Observatories , 2006, OCEANS 2006.

[14]  Yi Chao,et al.  High-resolution real-time modeling of the marine atmospheric boundary layer in support of the AOSN-II field campaign , 2009 .

[15]  Paul J. Martin,et al.  Description of the Navy Coastal Ocean Model Version 1.0 , 2000 .

[16]  Igor Shulman,et al.  Assimilation of HF Radar-Derived Radials and Total Currents in the Monterey Bay Area , 2009 .

[17]  Nicholas M. Patrikalakis,et al.  Adaptive Coupled Physical and Biogeochemical Ocean Predictions: A Conceptual Basis , 2004, International Conference on Computational Science.

[18]  D. Haidvogel,et al.  A semi-implicit ocean circulation model using a generalized topography-following coordinate system , 1994 .

[19]  F. Bretherton,et al.  A technique for objective analysis and design of oceanographic experiments applied to MODE-73 , 1976 .

[20]  Naomi Ehrich Leonard,et al.  Collective Motion, Sensor Networks, and Ocean Sampling , 2007, Proceedings of the IEEE.

[21]  Sylvie Malardel,et al.  Adaptive Observations: A Feasibility Study , 1999 .

[22]  Naomi Ehrich Leonard,et al.  Cooperative Control for Ocean Sampling: The Glider Coordinated Control System , 2008, IEEE Transactions on Control Systems Technology.

[23]  David E. Dietrich,et al.  Offshore propagation of eddy kinetic energy in the California Current , 2001 .

[24]  J. G. Bellingham,et al.  Guest editorial - autonomous ocean-sampling networks , 2001 .

[25]  Jerrold E. Marsden,et al.  The correlation between surface drifters and coherent structures based on high-frequency radar data in Monterey Bay , 2009 .

[26]  Allan R. Robinson,et al.  Multiscale Processes and Nonlinear Dynamics of the Circulation and Upwelling Events off Monterey Bay , 2009 .

[27]  Michael R. Benjamin,et al.  Autonomous Underwater Vehicles: Trends and Transformations , 2005 .

[28]  Naomi Ehrich Leonard,et al.  Routing strategies for underwater gliders , 2009 .

[29]  Naomi Ehrich Leonard,et al.  Preparing to predict: The Second Autonomous Ocean Sampling Network (AOSN-II) experiment in the Monterey Bay , 2009 .