Volumetric reconstruction of oceanographic fields estimated from remote sensing and in situ observations from autonomous underwater vehicles of opportunity

IEEE Journal of Oceanic Engineering

Volumn 36, Issue 1, 2011, Pages 12-24

  Alvarez, Alberto ^a  

^a NATO UNDERSEA RESEARCH CENTRE   (Italy)

Author keywords

Autonomous underwater vehicles (AUVs); finite elements; oceanographic sampling

Indexed keywords

AUTONOMOUS VEHICLES; DATA FUSION; ENVIRONMENTAL TECHNOLOGY; FINITE ELEMENT METHOD; REMOTE SENSING;

AUTONOMOUS UNDERWATER VEHICLES (AUVS); ENVIRONMENTAL ASSESSMENT; ENVIRONMENTAL INFORMATION; MONITORING TECHNOLOGIES; OCEANOGRAPHIC CONDITIONS; REMOTE SENSING INFORMATION; REMOTE SENSING PLATFORMS; VOLUMETRIC RECONSTRUCTION;

AUTONOMOUS UNDERWATER VEHICLES;

EID: 79952993781     PISSN: 03649059     EISSN: None     Source Type: Journal    
DOI: 10.1109/JOE.2010.2092472     Document Type: Article

References (43)

1. D. M. Barker,W. Huang, Y.-R. Guo, A. J. Bourgeois, and Q. N. Xiao, "A three-dimensional variational data assimilation system for MM5: Implementation and initial results," Monthly Weather Rev., vol. 132, no. 4, pp. 897-914, 2004. (Pubitemid 38502754)
2. S. L. Barnes, "A technique for maximizing details in numericalweather map analysis," J. Appl. Meteor., vol. 3, no. 4, pp. 396-409, 1964.
3. M. Berman, "Automated smoothing of image and other regularly spaced data," IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 5, pp. 460-468, May 1994.
4. M. W. Berry and K. S. Minser, "Algorithm 798: High dimensional interpolation using the modified Shepard method," ACM Trans. Math. Softw., vol. 25, no. 3, pp. 353-366, 1999. (Pubitemid 129533745)
5. E. Bohnsack, "Continuous field approximation of experimentally given data by finite elements," Comput. Structures, vol. 63, no. 6, pp. 1195-1204, 1997. (Pubitemid 127393724)
6. P. Brasseur, "A variational inverse method for the reconstruction of general circulation fields in the northern Bering Sea," J. Geophys. Res., vol. 96, no. C3, pp. 4891-4907, 1991.
7. P. Brasseur and J. A. Haus, "Application of a 3-D variational inverse model to the analysis of ecohydrodynamic data in the Northern Bering and Southern Chucki Seas," J. Mar. Syst., vol. 1, no. 4, pp. 383-401, 1991.
8. A. Caiti, A. Munafò, and R. Viviani, "Cooperating autonomous underwater vehicles to estimate ocean environmental parameters," presented at the IARP Int. Workshop Underwater Robot., Genova, Italy, Nov. 7-9, 2005.
9. J.-P. Chiles and P. Delfiner, Geostatistics: Modeling Spatial Uncertainty. New York: Wiley, 1999, pp. 720-720.
10. C. Courtier, J. Derber, R. Errico, J. F. Louis, and T. Vukicevic, "Important literature on the use of adjoint, variationalmethods and the Kalman filter in meteorology," Tellus, vol. 45A, no. 5, pp. 342-357, 1993. (Pubitemid 24400136)
11. T. Curtin, J. Bellingham, J. Catipovic, and D. Webb, "Autonomous oceanographic sampling networks," Oceanography, vol. 6, no. 3, pp. 86-94, 1993.
12. R. Daley, Atmospheric Data Analysis. New York: Cambridge Univ. Press, 1999, pp. 457-457.
13. G. Dhatt and G. Touzot, The Finite Element Displayed. NewYork: Wiley, 1984, pp. 509-509.
14. S. Dobrici and N. Pinardi, "An oceanographic three-dimensional variational data assimilation scheme," Ocean Model., vol. 22, no. 3-4, pp. 89-105, 2008.
15. Z. Feng and R. E. Rowlands, "Continuous full-field representation and differentiation of three-dimensional experimental vector data," Comput. Structures, vol. 26, no. 6, pp. 979-990, 1987.
16. R. L. Hardy, "Multiquadratic equations of topography and other irregular surfaces," J. Geophys. Res., vol. 76, no. 8, pp. 1905-1976, 1971.
17. C. Harris, "The omniscient eye: Satellite imagery, "battlespace awareness" and the structures of the imperial gaze," Surveillance Soc., vol. 4, no. 1-2, pp. 101-122, 2006.
18. X.-Y. Huang, Q. Xiao, D. M. Barker, X. Zhang, J. Michalakes, W. Huang, T. Henderson, J. Bray, Y. Chen, Z. Ma, J. Dudhia, Y. Guo, X. Zhang, D.-J. Won, H.-C. Lin, and Y.-H. Kuo, "Four-dimensional variational data assimilation for WRF: Formulation and preliminary results," Monthly Weather Rev., vol. 137, no. 1, pp. 299-314, 2009.
19. H. Inoue, "A least-squares smooth fitting for irregularly spaced data: Finite element approach using the B-cubic spline basis," Geophysics, vol. 51, no. 11, pp. 2051-2066, 1986. (Pubitemid 17477645)
20. J. Jagiello, "Stable numerical solution of the adsorption integral equation using splines," Langmuir, vol. 10, no. 8, pp. 2778-2785, 1994.
21. J. Jordan, "Remus AUV plays key role in IraqWar," Underwater Magazine, 2003 [Online]. Available: http://www.best-maritime-crewing.info/ catalogue-companies-list/company-source-48531-14.html
22. A. Katz, Principles of Statistical Mechanics-The Information Theory Approach. San Francisco, CA: W. H. Freeman, 1967, pp. 188-188.
23. F. Le Dimet and O. Talagrand, "Variational algorithms for analysis and assimilation of meteorological observations: Theoretic aspects," Tellus, vol. 38A, no. 2, pp. 97-110, 1986.
24. S. Levedev, A. Sirota, D. Medvedev, S. Khlebnikova, S. Vignudelli, H. M. Snaith, P. Cipollini, F. Venuti, F. Lyard, J. Bouffard, J. F. Cretaux, F. Birol, L. Roblou, A. Kostianoy, A. Ginzburg, N. Sheremet, E. Kuzmina, R. Mamedov, K. Ismatova, A. Alyev, and B Mustafayev, "Exploiting satellite altimetry in coastal ocean through the ALTICORE project," Rus. J. Earth. Sci., vol. 10, 2008, ES1002.
25. S. Z. Li, Markov Random Field Modeling in Computer Vision. New- York: Springer-Verlag, 1995, pp. 264-264.
26. Z. Li, Y. Chao, J. McWilliams, and K. Ide, "A three dimensional variational data assimilation scheme for the regional ocean modeling system: Implementation and basic experiments," J. Geophys. Res., vol. 113, 2008, DOI: 10.1029/2006JC004042, C05002.
27. B. Luong, J. Blum, and J. Verron, "A variational method for the resolution of a data assimilation problem in oceanography," Inverse Problems, vol. 14, no. 4, pp. 979-997, 1998. (Pubitemid 128557055)
28. D. Maes and D. Behringer, "Using satellite-derived sea level and temperature profiles for determining the salinity variability: A new approach," J. Geophys. Res., vol. 105, no. C4, pp. 8537-8547, 2000.
29. P. C. McIntosh, "Oceanographic data interpolation: Objective analysis and splines," J. Geophys. Res., vol. 95, no. C8, pp. 13529-13541, 1990.
30. A. Pascual and D. Gomis, "Use of surface data to estimate geostrophic transport," J. Atmos. Ocean. Technol., vol. 20, no. 6, pp. 912-926, 2003. (Pubitemid 36844888)
31. H. Pettersson, R/V Aranda Cruise Report 04/2009, Finnish Environment Institute, Helsinki, Finland, 2009, pp. 1-14.
32. U. Raudsepp, "Interannual and seasonal temperature variations in the Gulf of Riga and corresponding saline water inflow from the Baltic proper," Nordic Hydrology, vol. 32, no. 2, pp. 135-160, 2001. (Pubitemid 32667967)
33. J. A. Rice,Mathematical Statistics and Data Analysis. Belmont,CA: Duxbury Press, 1995, pp. 650-650.
34. A. R. Robinson, J. Sellschopp, W. G. Leslie, A. Alvarez, G. Baldasserini, P. J. Haley, P. F. J. Lermusiaux, C. J. Lozano, E. Nacini, R. Onken, R. Stoner, and P. Zanasca, "Forecasting synoptic transients in the Eastern Ligurian Sea," in Rapid Environmental Assessment, SACLANTCEN Conference Proceedings Series CP-46, E. Bovio, R. Tyce, and H. Schmidt, Eds., SACLANTCEN, La Spezia, Italy, 2003.
35. K. Shaw and N. Brown, "Unmanned vehicles enter the underwater battlespace," Jane's Navy Int., 2002 [Online]. Available: http://search. janes.com/
36. R. Scott, "Future undersea battlespace: Unmanned, undersea," Jane's Defense Weekly, vol. 37, no. 24, pp. 28-34, 2002.
37. D. Shepard, "A two dimensional interpolation function for irregularly spaced data," in Proc. 23rd ACM Nat. Conf., New York, 1968, pp. 517-524.
38. L. M. Stein, Interpolation of Spatial Data. New York: Springer- Verlag, 1999, pp. 246-246.
39. A. Tarantola, Inverse Problem Theory. Philadelphia, PA: SIAM, 2005, pp. 342-342.
40. A. M. Thompson, "Estimating the sun's rotation from solar oscillations by regularisation," Astron. Astrophys., vol. 265, no. 1, pp. 289-295, 1992.
41. O. C. Zienkiewicz and R. L. Taylor, The Finite Element Method. Basic Formulation and Linear Problems. Maidenhead, U.K.: Mc- Graw-Hill, 1995, pp. 648-648.
42. G. Wahba, Spline Models for Observational Data. Philadelphia, PA: SIAM, 1990, pp. 165-165.
43. G. Wahba, D. R. Johnson, F. Gao, and J. Gong, "Adaptive tuning of numerical weather prediction models: Randomized GCV in three-and four-dimensional data assimilation," Monthly Weather Rev., vol. 123, no. 11, pp. 3358-3369, 1995.