Optimum sampling designs for a glider-mooring observing network

Journal of Atmospheric and Oceanic Technology

Volumn 29, Issue 4, 2012, Pages 601-612

  Alvarez, A ^a   Mourre, B ^a  

^a NATO UNDERSEA RESEARCH CENTRE   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE STUDIES; CONTROL FIELDS; E-OPTIMALITY; EIGEN-VALUE; ENVIRONMENTAL CHARACTERIZATION; ERROR COVARIANCE MATRIX; GEOSCIENCES; INFORMATION CONTENTS; MARINE ENVIRONMENT; OBSERVING SYSTEMS; OCEAN OBSERVING SYSTEMS; OPTIMAL CRITERIA; OPTIMAL DESIGN; SAMPLING DESIGN; SAMPLING STRATEGIES; SPATIAL VARIABILITY; STUDY AREAS; TEMPORAL VARIABILITY; WESTERN MEDITERRANEAN;

COVARIANCE MATRIX; EIGENVALUES AND EIGENFUNCTIONS; GLIDERS; MOORING;

OPTIMIZATION;

DESIGN; EIGENVALUE; INSTRUMENTATION; MOORING SYSTEM; OBSERVATIONAL METHOD; OCEANOGRAPHY; OPTIMIZATION; PERFORMANCE ASSESSMENT; SAMPLING; SPATIAL VARIATION; UNCERTAINTY ANALYSIS;

LIGURIAN SEA; MEDITERRANEAN SEA;

EID: 84864769064     PISSN: 07390572     EISSN: 15200426     Source Type: Journal    
DOI: 10.1175/JTECH-D-11-00105.1     Document Type: Article

References (53)

1. Alvarez, A., 2011: Volumetric reconstruction of oceanographic fields estimated from remote sensing and in situ observations from AUVs of opportunity. IEEE J. Oceanic Eng., 36, 12-24.
2. Alvarez, A., and B.Mourre, 2011: Optimumand adaptivemission planning of gliders. Proc. Fifth European Glider Observatories Meeting, Canary Island, Spain, European/Everyone's Gliding Observatories Network. [Available on http://ego2011.eu/doc/PDF-EGO/Friday%2018th/EGO%202011%20-%20Alberto%20Alvarez%201.pdf.]
3. Alvarez, A., B. Garau, and A. Caiti, 2007: Combining networks of drifting profiling floats and gliders for adaptive sampling of the ocean. Proc. 2007 IEEE Int. Conf. on Robotics and Automation, Rome, Italy, IEEE, 157-162.
4. Aster, R. C., B. Borchers, and C. H. Thurber, 2005: Parameter Estimation and Inverse Problems. Elsevier Academic Press, 291 pp.
5. Astraldi, M., and G. P. Gasparini, 1992: The seasonal characteristics of the circulation in the north Mediterranean basin and their relationship with the atmospheric-climatic conditions. J. Geophys. Res., 97 (C6), 9531-9540.
6. Ballabrera-Poy, J., E. Hackert, R. Murtugudde, and A. J. Busalacchi, 2007: An observing system simulation experiment for an optimal moored instrument array in the tropical Indian Ocean. J. Climate, 20, 3284-3299.
7. Barnes, R. J., 1989: Sampling design for geologic site characterization. Geostatistics: Proceedings of the Third International Geostatistics Congress, Vol. 2, M. Amstrong, Ed., Kluwer Academic Publishers, 809-822.
8. Barth, N., 1992: Oceanographic experiment design II: Genetic algorithms. J. Atmos. Oceanic Technol., 9, 434-443.
9. Barth, N., and C. Wunsch, 1990: Oceanographic experiment design by simulated annealing. J. Atmos. Oceanic Technol., 20, 1249- 1263.
10. Bretherton, F. P., R. B. Davies, and C. E. Fandry, 1976: A technique for objective analysis and design of oceanographic experiments applied to MODE-73. Deep-Sea Res., 23, 582-599.
11. Coope, I. D., and C. J. Price, 2002: Positive bases in numerical optimization. Comput. Optim. Appl., 21, 169-175.
12. Cummings, J., 2005: Operational multivariate ocean data assimilation. Quart. J. Roy. Meteor. Soc., 131, 3583-3604.
13. Curtin, T., and J.-G. Bellingham, 2009: Progress towards autonomous ocean sampling networks. Deep-Sea Res. II, 56, 62-67.
14. Curtin, T., J.-G. Bellingham, J. Catipovic, and D. Webb, 1993: Autonomous oceanographic sampling networks. Oceanography, 6 (3), 86-94.
15. Davies, R. E., 1991: Observing the general circulation with floats. Deep-Sea Res., 38, 531-571.
16. Dhatt, G., and G. Touzot, 1984: The Finite Element Displayed. John Wiley & Sons, 509 pp.
17. Dickey, T.-D., 2003: Emerging ocean observations for interdisciplinary data assimilation systems. J. Mar. Syst., 40-41C, 5-48.
18. Evensen, G., 2003: The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn., 53, 343-367.
19. Galea, A. M., 1999: Various methods for obtaining the optimal path for a glider vehicle in shallow water and high currents. Proc. Int. Conf. Unmanned Untethered Submersible Technology, Durham, NH, Autonomous Undersea System Institute, 150-161.
20. Gargett, A. E., and J. Marra, 2002: Effects of upper ocean physical processes turbulence, advection, and air-sea interaction on oceanic primary production. The Sea, A. Robinson, B. Rothschild, and J. McCarthy, Eds., Biological-Physical Interactions in the Sea, Vol. 12, John Wiley & Sons, 19-49.
21. Griffiths,G., S. Fielding, and H. S. J. Roe, 2005: Biological-physical- acoustical interactions, The Sea, A. Robinson, B. Rothschild, and J. McCarthy, Eds., Biological-Physical Interactions in the Sea, Vol. 12, John Wiley & Sons, 441-474.
22. Hackert, E. C., R. N. Miller, and A. J. Busalacchi, 1998: An optimized design for a moored instrument array in the tropical Atlantic Ocean. J. Geophys. Res., 103, 7491-7509.
23. Heaney, K. D.,G.Gawarkiewicz, T. F. Duda, and P. F. J. Lermusiaux, 2007: Non-linear optimization of autonomous undersea vehicle sampling strategies for oceanographic data-assimilation. J. Field Robot., 24 (Special Issue), 437-448.
24. Hernandez, F., P. Y. Le Traon, and N. H. Barth, 1995: Optimizing a drifter cast strategy with a genetic algorithm. J. Atmos. Oceanic Technol., 12, 330-345.
25. Hirschi, J., J. Baehr, J. Marotzke, J. Stark, S. Cunningham, and J. O. Beismann, 2003: A monitoring design for the Atlantic meridional overturning circulation. Geophys. Res. Lett., 30, 1413, doi:10.1029/2002GL016776.
26. Hooke, R., and T. A. Jeeves, 1961: Direct search solution of numerical and statistical problems. J. Assoc. Comp. Mach., 8, 212-229.
27. Jones, E. M., P. R. Oke, F. Rizwi, and L. Murray, 2012: Assimilation of glider and mooring data into a coastal ocean model. Ocean Modell., 47, 1-13, doi:10.1016/j.ocemod.2011.12.009.
28. Kiefer, J., and J. Wofowitz, 1960: The equivalence of two extremum problems. Can. J. Math., 12, 363-366.
29. Kuragano, T., and M. Kamachi, 2000: Global statistical space-time scales of oceanic variability estimated from the TOPEX/POSEIDON altimeter data. J. Geophys. Res., 105, 955-974.
30. Leonard,N. E., D. Paley, F. Lekien,R. Sepulchre,D.M. Frantantoni, and R. Davis, 2007: Collective motion, sensor networks and ocean sampling. Proc. IEEE, 95, 48-74.
31. Leonard,N. E., D. Paley, R. E. Davis, D. M. Fratantoni, F. Lekien, and F. Zhang, 2010: Coordinated control of an underwater glider fleet in an adaptive ocean sampling field experiment in Monterey Bay. J. Field Robot., 27, 718-740.
32. Lermusiaux, P. F. J., C. Evangelinos, R. Tian, P. J. Haley, J. J. McCarthy,N.M. Patrikalakis, A. R. Robinson, andH. Schmidt, 2004: Adaptive coupled physical and biogeochemical ocean predictions:Aconceptual basis. Computational Science-ICCS 2004: Proceedings of the Fourth International Conference, F.Darema, Ed., Lecture Notes inComputer Science,Vol. 3038, Springer, 685-692.
33. Le Traon, P. Y., and F. Hernandez, 1992: Mapping the oceanic mesoscale circulation: Validation of satellite altimetry using surface drifters. J. Atmos. Oceanic Technol., 9, 687-698.
34. Malone, T. C., and M. Cole, 2000: Toward a global scale coastal ocean observing system. Oceanography, 13, 7-11.
35. Martin, P. J., 2000: Description of the Navy Coastal Ocean Model Version 1.0. Naval Research Laboratory Rep. NRL/FR/7322- 00-9962, 42 pp.
36. Millot, C., 1987: Circulation in the Western Mediterranean Sea. Oceanol. Acta, 10, 143-149.
37. Mourre, B., P. De Mey, Y. Menard, F. Lyard, and C. Le Provost, 2006: Relative performance of future altimeter systems and tide gauges in constraining a model of North Sea highfrequency barotropic dynamics. Ocean Dyn., 56 (5-6), 473-486.
38. Muller, W. G., 2007: Collecting Spatial Data. Springer, 242 pp.
39. Oke, P. R., and A. Schiller, 2007: A model-based assessment and design of a tropical Indian Ocean mooring array. J. Climate, 20, 3269-3283.
40. Ramp, S., and Coauthors, 2009: Preparing to predict: The Second Autonomous Ocean Sampling Network (AOSN-II) experiment in Monterey Bay. Deep-Sea Res. II, 56, 68-86.
41. Robinson, A. R., and S. M. Glenn, 1999: Adaptive sampling for ocean forecasting. Nav. Res. Rev., 51, 28-38.
42. Robinson, A. R., J. J. McCarthy, and B. J. Rothschild, 1999: Interdisciplinary ocean science is evolving and a systems approach is essential. J. Mar. Syst., 22, 231-239.
43. Rudnick, D. L., and S. T. Cole, 2011: On sampling the ocean using underwater gliders. J. Geophys. Res., 116, C08010, doi:10.1029/ 2010JC006849.
44. Rudnick, D. L., R. E. Davis, C. C. Eriksen, D. M. Frantantoni, and M. J. Perry, 2004: Underwater gliders for ocean research. Mar. Technol. Soc. J., 38, 48-59.
45. Shulman, I., D. J. McGillicuddy, M. A. Moline, S. H. D. Haddock, J. C. Kindle, D. Nechaev, and M. W. Phelps, 2005: Bioluminescence intensity modeling and sampling strategy optimization. J. Atmos. Oceanic Eng., 1267-1281.
46. Silvey, S. D., 1980: Optimal Design-An Introduction to the Theory for Parameter Estimation. Chapman and Hall, 86 pp.
47. Smith, R., Y. Chao, P. Li, D. Caron, B. Jones, and G. S. Sukhatme, 2010: Planning and implementing trajectories for autonomous underwater vehicles to track evolving ocean processes based on predictions from a regional ocean model. Int. J. Robot. Res., 29, 1475-1497.
48. Sournia, A., J. M. Brylinski, S. Dallot, P. Le Corre, M. Leveau, L. Prieur, and C. Froget, 1990: Fronts hydrologiques au large des cotes francaises: Les sites-ateliers du programme Frontal. Oceanol. Acta, 13, 413-438.
49. Stein, L. M., 1999: Interpolation of Spatial Data. Springer-Verlag, 246 pp.
50. Stommel, H., 1989: The Slocum mission. Oceanography, 2, 22-25.
51. Vignudelli, S., P. Cipollini, M. Astraldi, G. P. Gasparini, and G. Manzella, 2000: Integrated use of altimeter and in situ data for understanding the water exchanges between the Tyrrhenian and Ligurian Seas. J. Geophys. Res., 105 (C8), 19 649-19 663.
52. Wallace, D., 2009: The surface ocean-lower atmosphere study (SOLAS). Global Change Newsletter, Vol. 73, Pacific Institute for Development, Environment, Security, 6-8.
53. Zienkiewicz, O. C., and R. L. Taylor, 1995: The finite element method. Basic Formulation and Linear Problems, O. C. Zienkiewicz and R. L. Taylor, Eds., McGraw-Hill, 648 pp