Oceanography The Official Magazine of
The Oceanography Society
Volume 30 Issue 02

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Volume 30, No. 2
Pages 172 - 185

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Optimal Planning and Sampling Predictions for Autonomous and Lagrangian Platforms and Sensors in the Northern Arabian Sea

By Pierre F.J. Lermusiaux , Patrick J. Haley Jr., Sudip Jana, Abhinav Gupta, Chinmay S. Kulkarni, Chris Mirabito, Wael Hajj Ali, Deepak N. Subramani, Arkopal Dutt, Jing Lin, Andrey Y. Shcherbina, Craig M. Lee, and Avijit Gangopadhyay 
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Article Abstract

Where, when, and what to sample, and how to optimally reach the sampling locations, are critical questions to be answered by autonomous and Lagrangian platforms and sensors. For a reproducible scientific sampling approach, answers should be quantitative and provided using fundamental principles. This article reviews concepts and recent progress toward this principled approach, focusing on reachability, path planning, and adaptive sampling, and presents results of a real-time forecasting and planning experiment completed during February–April 2017 for the Northern Arabian Sea Circulation-autonomous research program. The predictive skill, layered fields, and uncertainty estimates obtained using the MIT MSEAS multi-resolution ensemble ocean modeling system are first studied. With such inputs, deterministic and probabilistic three-dimensional reachability forecasts issued daily for gliders and floats are then showcased and validated. Finally, a Bayesian adaptive sampling framework is shown to forecast in real time the observations that are most informative for estimating classic ocean fields and also secondary variables such as Lagrangian coherent structures.

Citation

Lermusiaux, P.F.J., P.J. Haley Jr., S. Jana, A. Gupta, C.S. Kulkarni, C. Mirabito, W.H. Ali, D.N. Subramani, A. Dutt, J. Lin, A.Y. Shcherbina, C.M. Lee, and A. Gangopadhyay. 2017. Optimal planning and sampling predictions for autonomous and Lagrangian platforms and sensors in the northern Arabian Sea. Oceanography 30(2):172–185, https://doi.org/10.5670/oceanog.2017.242.

References
    Bellingham, J.G., and K. Rajan. 2007. Robotics in remote and hostile environments. Science 318(5853):1,098–1,102, https://doi.org/​10.1126/science.1146230.
  1. Bocquet, M., C.A. Pires, and L. Wu. 2010. Beyond Gaussian statistical modeling in geophysical data assimilation. Monthly Weather Review 138(8):2,997–3,023.
  2. Burchard, H., L. Debreu, K. Klingbeil, and F. Lemarié. 2017. The numerics of hydrostatic structured-grid coastal ocean models: State of the art and future perspectives. HAL-Inria, ID: hal-01443357.
  3. Centurioni, L.R., V. Hormann, L.D. Talley, I. Arzeno, L. Beal, M. Caruso, P. Conry, R. Echols, H.J.S. Fernando, S.N. Giddings, and others. 2017. Northern Arabian Sea Circulation-Autonomous Research (NASCar): A research initiative based on autonomous sensors. Oceanography 30(2):74–87, https://doi.org/​10.5670/oceanog.2017.224.
  4. Cover, T.M., and J.A. Thomas. 1991. Elements of Information Theory. John Wiley & Sons, 576 pp.
  5. Cummings, J.A., and O.M. Smedstad. 2013. Variational data assimilation for the global ocean. Pp. 303–343 in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, vol. II. Springer Berlin Heidelberg.
  6. Curtin, T.B., and J.G. Bellingham. 2009. Progress toward autonomous ocean sampling networks. Deep Sea Research Part II 56(3):62–67, https://doi.org/​10.1016/j.dsr2.2008.09.005.
  7. Curtin, T.B., J.G. Bellingham, J. Catipovic, and D. Webb. 1993. Autonomous oceanographic sampling networks. Oceanography 6(3):86–94, https://doi.org/10.5670/oceanog.1993.03.
  8. Cushman-Roisin, B., and J.M. Beckers. 2011. Introduction to Geophysical Fluid Dynamics: Physical and Numerical Aspects, vol. 101, 2nd ed. Academic Press, 875 pp.
  9. Deleersnijder, E., V. Legat, and P.F.J. Lermusiaux. 2010. Multi-scale modelling of coastal, shelf and global ocean dynamics. Ocean Dynamics 60:1,357–1,359, https://doi.org/10.1007/s10236-010-0363-6.
  10. Deleersnijder, E., and P.F.J. Lermusiaux. 2008. Multi-scale modelling: Nested grid and unstructured mesh approaches. Ocean Dynamics 58:335–336, https://doi.org/10.1007/s10236-008-0170-5.
  11. Dickey, T.D. 2003. Emerging ocean observations for interdisciplinary data assimilation systems. Journal of Marine Systems 40:5–48, https://doi.org/10.1016/S0924-7963(03)00011-3.
  12. Dickey, T.D., E.C. Itsweire, M.A. Moline, and M.J. Perry. 2008. Introduction to the Limnology and Oceanography special issue on autonomous and Lagrangian platforms and sensors (ALPS). Limnology and Oceanography 53, https://doi.org/​10.4319/lo.2008.53.5_part_2.2057.
  13. Edwards, J., J. Smith, A. Girard, D. Wickman, P.F.J. Lermusiaux, D.N. Subramani, P.J. Haley Jr., C. Mirabito, C.S. Kulkarni, and S. Jana. In press. Data-driven learning and modeling of AUV operational characteristics for optimal path planning. In Oceans ‘17 MTS/IEEE, Aberdeen, June 19–22, 2017.
  14. Egbert, G.D., and S.Y. Erofeeva. 2002. Efficient inverse modeling of barotropic ocean tides. Journal of Atmospheric and Oceanic Technology 19(2):183–204, https://doi.org/10.1175/​1520-0426(2002)019<0183:EIMOBO>2.0.CO;2.
  15. Egbert, G.D., and S.Y. Erofeeva. 2013. TPX08-Atlas. OSU Tidal Data Inversion, http://volkov.oce.orst.edu/tides/tpxo8_atlas.html.
  16. Environmental Modeling Center. 2003. The GFS Atmospheric Model. NCEP Office Note 442, Global Climate and Weather Modeling Branch, EMC, Camp Springs, Maryland.
  17. Feppon, F., and P.F.J. Lermusiaux. In press. Dynamically orthogonal numerical schemes for efficient stochastic advection and Lagrangian transport. SIAM Review.
  18. Haley, P.J. Jr., A. Agarwal, and P.F.J. Lermusiaux. 2015. Optimizing velocities and transports for complex coastal regions and archipelagos. Ocean Modelling 89:1–28, https://doi.org/10.1016/​j.ocemod.2015.02.005.
  19. Haley, P.J. Jr., and P.F.J. Lermusiaux. 2010. Multiscale two-way embedding schemes for free-surface primitive-equations in the Multidisciplinary Simulation, Estimation and Assimilation System. Ocean Dynamics 60:1,497–1,537, https://doi.org/​10.1007/s10236-010-0349-4.
  20. Haller, G. 2015. Lagrangian coherent structures. Annual Review of Fluid Mechanics 47:137–162, https://doi.org/10.1146/annurev-fluid-010313-141322.
  21. Hogan, T.F., M. Liu, J.A. Ridout, M.S. Peng, T.R. Whitcomb, B.C. Ruston, C.A. Reynolds, S.D. Eckermann, J.R. Moskaitis, N.L. Baker, and others. 2014. The Navy Global Environmental Model. Oceanography 27(3):116–125, https://doi.org/​10.5670/oceanog.2014.73.
  22. Jackson, P. 1998. Introduction to Expert Systems, 3rd ed. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 542 pp.
  23. Leonard, J.J., and A. Bahr. 2016. Autonomous underwater vehicle navigation. Pp. 341–358 in Springer Handbook of Ocean Engineering. Springer International Publishing.
  24. Leonard, N.E., D.A. 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. Journal of Field Robotics 27(6):718–740, https://doi.org/10.1002/rob.20366.
  25. Leonard, N.E., D.A. Paley, F. Lekien, R. Sepulchre, D.M. Fratantoni, and R.E. Davis. 2007. Collective motion, sensor networks, and ocean sampling. Proceedings of the IEEE 95(1):48–74, https://doi.org/10.1109/JPROC.2006.887295.
  26. Lermusiaux, P.F.J. 1999. Estimation and study of mesoscale variability in the Strait of Sicily. Dynamics of Atmospheres and Oceans 29(2):255–303, https://doi.org/10.1016/S0377-0265(99)00008-1.
  27. Lermusiaux, P.F.J. 2002. On the mapping of multivariate geophysical fields: Sensitivity to size, scales and dynamics. Journal of Atmospheric and Oceanic Technology 19:1,602–1,637, https://doi.org/10.1175/1520-0426(2002)019​<1602:OTMOMG>2.0.CO;2.
  28. Lermusiaux, P.F.J. 2006. Uncertainty estimation and prediction for interdisciplinary ocean dynamics. Journal of Computational Physics 217:176–199, https://doi.org/10.1016/j.jcp.2006.02.010.
  29. Lermusiaux, P.F.J. 2007. Adaptive modeling, adaptive data assimilation and adaptive sampling. Physica D 230:172–196, https://doi.org/10.1016/​j.physd.2007.02.014.
  30. Lermusiaux, P.F.J., D.G.M. Anderson, and C.J. Lozano. 2000. On the mapping of multivariate geophysical fields: Error and variability subspace estimates. Quarterly Journal of the Royal Meteorological Society 126:1,387–1,430, https://doi.org/10.1002/qj.49712656510.
  31. Lermusiaux, P.F.J., C.-S. Chiu, G.G. Gawarkiewicz, P. Abbot, A.R. Robinson, R.N. Miller, P.J. Haley, W.G. Leslie, S.J. Majumdar, A. Pang, and F. Lekien. 2006a. Quantifying uncertainties in ocean predictions. Oceanography 19(1):90–103, https://doi.org/10.5670/oceanog.2006.93.
  32. Lermusiaux, P.F.J., P.J. Haley Jr., S. Jana, A. Gupta, C. Kulkarni, C. Mirabito, W.H. Ali, and D. Subramani. 2017. NASCar-OPS Sea Exercise 17, Arabian Sea: February–March 2017, http://mseas.mit.edu/Sea_exercises/NASCar-OPS-17.
  33. Lermusiaux, P.F.J, T. Lolla, P.J. Haley Jr., K. Yigit, M.P. Ueckermann, T. Sondergaard, and W.G. Leslie. 2016. Science of Autonomy: Time-optimal path planning and adaptive sampling for swarms of ocean vehicles. Pp. 481–498 in Springer Handbook of Ocean Engineering. T. Curtin, ed., https://doi.org/10.1007/978-3-319-16649-0_21.
  34. Lermusiaux, P.F.J., P. Malanotte-Rizzoli, D. Stammer, J. Carton, J. Cummings, and A.M. Moore. 2006b. Progress and prospects of U.S. data assimilation in ocean research. Oceanography 19(1):172–183, https://doi.org/10.5670/oceanog.2006.102.
  35. Lermusiaux, P.F.J., and A.R. Robinson. 1999. Data assimilation via Error Subspace Statistical Estimation: Part I: Theory and schemes. Monthly Weather Review 127(7):1,385–1,407, https://doi.org/10.1175/1520-0493(1999)127​<1385:DAVESS>2.0.CO;2.
  36. Lermusiaux, P.F.J., A.R. Robinson, P.J. Haley, and W.G. Leslie, 2002. Advanced interdisciplinary data assimilation: Filtering and smoothing via error subspace statistical estimation. Pp. 795–802 in Proceedings of OCEANS 2002 MTS/IEEE, April 29, 2003. Holland Publications, https://doi.org/10.1109/OCEANS.2002.1192071.
  37. Lermusiaux, P.F.J., D.N. Subramani, J. Lin, C.S. Kulkarni, A. Gupta, A. Dutt, T. Lolla, P.J. Haley Jr., W.H. Ali, C. Mirabito, S. Jana. In press. A Future for Intelligent Autonomous Ocean Observing Systems. The Sea.
  38. Leslie, W.G., P.J. Haley Jr., P.F.J. Lermusiaux, M.P. Ueckermann, O. Logutov, and J. Xu. 2010. MSEAS Manual. MSEAS Report-06, Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts.
  39. Lolla, T. 2016. Path Planning and Adaptive Sampling in the Coastal Ocean. 2016. PhD Thesis, Massachusetts Institute of Technology, Department of Mechanical Engineering.
  40. Lolla, T., P.J. Haley Jr., and P.F.J. Lermusiaux. 2014a. Time-optimal path planning in dynamic flows using level set equations: Realistic applications. Ocean Dynamics 64(10):1,399–1,417, https://doi.org/10.1007/s10236-014-0760-3.
  41. Lolla, T., P.J. Haley Jr., and P.F.J. Lermusiaux. 2015. Path planning in multi-scale ocean flows: Coordination and dynamic obstacles. Ocean Modelling 94:46–66, https://doi.org/10.1016/​j.ocemod.2015.07.013.
  42. Lolla, T., and P.F.J. Lermusiaux. 2017a. A Gaussian mixture model smoother for continuous nonlinear stochastic dynamical systems: Theory and scheme. Monthly Weather Review 145:2,743–2,761, https://doi.org/10.1175/MWR-D-16-0064.1.
  43. Lolla, T., and P.F.J. Lermusiaux. 2017b. A Gaussian mixture model smoother for continuous nonlinear stochastic dynamical systems: Applications. Monthly Weather Review 145:2,763–2,790 https://doi.org/10.1175/MWR-D-16-0065.1.
  44. Lolla, T., P.F.J. Lermusiaux, M.P. Ueckermann, and P.J. Haley Jr. 2014b. Time-optimal path planning in dynamic flows using level set equations: Theory and schemes. Ocean Dynamics 64(10):1,373–1,397, https://doi.org/10.1007/s10236-014-0757-y.
  45. Mirabito, C., D.N. Subramani, T. Lolla, P.J. Haley Jr., A. Jain, P.F.J. Lermusiaux, C. Li, D.K.P. Yue, Y. Liu, F.S. Hover, and others. In press. Autonomy for surface ship interception. In Oceans ‘17 MTS/IEEE. Aberdeen, June 19–22, 2017.
  46. Nicholson, J.W., and A.J. Healey. 2008. The present state of autonomous underwater vehicle (AUV) applications and technologies. Marine Technology Society Journal 42(1):44–51, https://doi.org/10.4031/002533208786861272.
  47. Paley, D.A., F. Zhang, and N.E. Leonard. 2008. Cooperative control for ocean sampling: The glider coordinated control system. IEEE Transactions on Control Systems Technology 16(4):735–744, https://doi.org/10.1109/TCST.2007.912238.
  48. Ramp, S.R., R.E. Davis, N.E. Leonard, I. Shulman, Y. Chao, A.R. Robinson, J. Marsden, P.F.J. Lermusiaux, D. Fratantoni, J.D. Paduan, and others. 2009. Preparing to predict: The second Autonomous Ocean Sampling Network (AOSN-II) Experiment in the Monterey Bay. Deep Sea Research Part II 56:68–86, https://doi.org/10.1016/​j.dsr2.2008.08.013.
  49. Reich, S., and C. Cotter. 2015. Probabilistic Forecasting and Bayesian Data Assimilation. Cambridge University Press, 308 pp.
  50. Ringler, T., M. Petersen, R.L. Higdon, D. Jacobsen, P.W. Jones, and M. Maltrud. 2013. A multi-resolution approach to global ocean modeling. Ocean Modelling 69:211–232, https://doi.org/10.1016/​j.ocemod.2013.04.010.
  51. Robinson, A.R., J. Sellschopp, W.G. Leslie, A. Alvarez, G. Baldasserini, P.J. Haley, P.F.J. Lermusiaux, C.J. Lozano, E. Nacini, R. Onken, and others. 2003. Forecasting synoptic transients in the Eastern Ligurian Sea. In Rapid Environmental Assessment. E. Bovio, R. Tyce, and H. Schmidt, eds, SACLANTCEN Conference Proceedings Series CP-46, Saclantcen, La Spezia, Italy.
  52. Roy, N., H.L. Choi, D. Gombos, J. Hansen, J. How, and S. Park. 2007. Adaptive observation strategies for forecast error minimization. Pp. 1,138–1,146 in Computational Science–ICCS 2007. Y. Shi, G.D. van Albada, J. Dongarra, and P.M.A. Sloot, eds, https://doi.org/10.1007/978-3-540-72584-8_149.
  53. Rudnick, D.L., R.E. Davis, C.C. Eriksen, D.M. Fratantoni, and M.J. Perry. 2004. Underwater gliders for ocean research. Marine Technology Society Journal 38:73–84, https://doi.org/10.4031/​002533204787522703.
  54. Rudnick, D.L., and M.J. Perry, eds. 2003. ALPS: Autonomous and Lagrangian Platforms and Sensors. Report of the Workshop held March 31–April 2, 2003, 64 pp, https://geo-prose.com/pdfs/alps_report.pdf.
  55. Russell, S., and P. Norvig. 2009. Artificial Intelligence: A Modern Approach. Prentice Hall, Upper Saddle River, NJ, 1,152 pp.
  56. Sapsis, T.P., and P.F.J. Lermusiaux. 2009. Dynamically orthogonal field equations for continuous stochastic dynamical systems. Physica D 238:2,347–2,360, https://doi.org/10.1016/j.physd.2009.09.017.
  57. Särkkä, S. 2013. Bayesian Filtering and Smoothing. Volume 3 of Institute of Mathematical Statistics Textbooks, Cambridge University Press, 232 pp.
  58. Schmidt, H., M.R. Benjamin, S.M. Petillo, and R. Lum. 2016. Nested autonomy for distributed ocean sensing. Pp. 459–480 in Springer Handbook of Ocean Engineering. Springer International Publishing.
  59. Schofield, O., S. Glenn, J. Orcutt, M. Arrott, M. Meisinger, A. Gangopadhyay, W. Brown, R. Signell, M. Moline, Y. Chao, and others. 2010. Automated sensor networks to advance ocean science. Eos Transactions, American Geophysical Union 91(39):345–346, https://doi.org/​10.1029/2010EO390001.
  60. Smith, W.H., and D.T. Sandwell. 1997. Global sea floor topography from satellite altimetry and ship depth soundings. Science 277(5334):1,956–1,962, https://doi.org/10.1126/science.277.5334.1956.
  61. Sondergaard, T., and P.F.J. Lermusiaux. 2013a. Data assimilation with Gaussian mixture models using the dynamically orthogonal field equations: Part I. Theory and scheme. Monthly Weather Review 141(6)1,737–1,760, https://doi.org/10.1175/MWR-D-11-00295.1.
  62. Sondergaard, T., and P.F.J. Lermusiaux, 2013b. Data assimilation with Gaussian mixture models using the dynamically orthogonal field equations: Part II. Applications. Monthly Weather Review 141(6):1,761–1,785, https://doi.org/10.1175/MWR-D-11-00296.1.
  63. Steinberg, M. 2006. Intelligent autonomy for unmanned naval vehicles. In Proceedings of SPIE 6230, Unmanned Systems Technology VIII. 623013, May 9, 2006, https://doi.org/10.1117/12.665870.
  64. Stommel, H. 1989. The Slocum mission. Oceanography 2(1):22–25, https://doi.org/​10.5670/oceanog.1989.26.
  65. Subramani, D.N., P.J. Haley Jr., and P.F.J. Lermusiaux. 2017. Energy-optimal path planning in the coastal ocean. Journal of Geophysical Research 122:3,981–4,003, https://doi.org/10.1002/​2016JC012231.
  66. Subramani, D.N., and P.F.J. Lermusiaux, 2016. Energy-optimal path planning by stochastic dynamically orthogonal level-set optimization. Ocean Modelling 100:57–77, https://doi.org/10.1016/​j.ocemod.2016.01.006.
  67. Subramani, D.N., P.F.J. Lermusiaux, P.J. Haley Jr., C. Mirabito, S. Jana, C.S. Kulkarni, A. Girard, D. Wickman, J. Edwards, and J. Smith. In press. Time-optimal path planning: Real-time sea exercises. In Oceans ‘17 MTS/IEEE. Aberdeen, June, 19–22 2017.
  68. Sun, W., P. Tsiotras, T. Lolla, D.N. Subramani, and P.J.F. Lermusiaux. 2017a. Pursuit-evasion games in dynamic flow fields via reachability set analysis. Pp. 4,595–4,600 in American Control Conference (ACC), 2017. May 24–26, 2017, Seattle, Washington, IEEE, https://doi.org/10.23919/ACC.2017.7963664.
  69. Sun, W., P. Tsiotras, T. Lolla, D.N. Subramani, and P.F.J. Lermusiaux. 2017b. Multiple-pursuer/one-evader pursuit-evasion game in dynamic flow fields. Journal of Guidance, Control and Dynamics 40(7):1,627–1,637, https://doi.org/​10.2514/1.G002125.
  70. Ueckermann, M.P., P.F.J. Lermusiaux, and T.P. Sapsis, 2013. Numerical schemes for dynamically orthogonal equations of stochastic fluid and ocean flows. Journal of Computational Physics 233:272–294, https://doi.org/10.1016/j.jcp.2012.08.041.
  71. Venkatesan R., A. Tandon, E. D’Asaro, and M.A. Atmanand, eds. 2017. Observing the Oceans in Real Time. Springer International Publishing, 400 pp.
  72. Wei, Q.J. 2015. Time-Optimal Path Planning in Uncertain Flow Fields Using Stochastic Dynamically Orthogonal Level Set Equations. B.S. Thesis, Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts.
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