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Validation of genetic algorithm-based optimal sampling for ocean data assimilation

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Abstract

Regional ocean models are capable of forecasting conditions for usefully long intervals of time (days) provided that initial and ongoing conditions can be measured. In resource-limited circumstances, the placement of sensors in optimal locations is essential. Here, a nonlinear optimization approach to determine optimal adaptive sampling that uses the genetic algorithm (GA) method is presented. The method determines sampling strategies that minimize a user-defined physics-based cost function. The method is evaluated using identical twin experiments, comparing hindcasts from an ensemble of simulations that assimilate data selected using the GA adaptive sampling and other methods. For skill metrics, we employ the reduction of the ensemble root mean square error (RMSE) between the “true” data-assimilative ocean simulation and the different ensembles of data-assimilative hindcasts. A five-glider optimal sampling study is set up for a 400 km × 400 km domain in the Middle Atlantic Bight region, along the New Jersey shelf-break. Results are compared for several ocean and atmospheric forcing conditions.

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References

  • Alvarez A, Mourre B (2012) Optimum sampling designs for a glider-mooring observing network. J Atmos Ocean Technol 29:601–612

    Article  Google Scholar 

  • Berliner LM, Lu Z-Q, Snyder C (1999) Statistical design for adaptive weather observations. J Atmos Sci 56(15):2536–2552

    Article  Google Scholar 

  • Bishop CH, Etherton BJ, Majumdar SJ (2001) Adaptive sampling with the ensemble transform Kalman filter. Part I: theoretical aspects. Mon Weather Rev 129:420–436

    Article  Google Scholar 

  • Carrassi A, Trevisan A, Uboldi F (2007) Adaptive observations and assimilation in the unstable subspace by breeding on the data-assimilation system. Tellus A 59(1):101–113

    Article  Google Scholar 

  • Chapman NR, Lynch JF (2010) Special issue on the 2006 shallow water experiment. IEEE J Ocean Eng 35(1):1–2

    Article  Google Scholar 

  • Cococcioni M, Lazzerini M, Lermusiaux PFJ (2015) Adaptive sampling using fleets of underwater gliders in the presence of fixed buoys using a constrained clustering algorithm. OCEANS 2015 MTS/IEEE. IEEE, Washington DC

    Google Scholar 

  • Collins MD, Kuperman WA (1991) Focalization: environmental focusing and source localization. J Acoust Soc Am 90(3):1410–1422

    Article  Google Scholar 

  • Collette, Y. and Siarry, P., 2013. Multiobjective optimization: principles and case studies. Springer Science & Business Media.

  • Curtin TB, Bellingham JG, Catipovic J, Webb D (1993) Autonomous oceanographic sampling networks. Oceanography 6(3):86–94

    Article  Google Scholar 

  • Edwards CA, Moore AM, Hoteit I, Cornuelle BD (2015) Regional ocean data assimilation. Annual review of marine science 7:21–42

    Article  Google Scholar 

  • Egbert GD, Erofeeva SY (2002) Efficient inverse modeling of barotropic ocean ties. J Atmos Ocean Technol 19(2):183–204

    Article  Google Scholar 

  • Ferri G, Cococcioni M, Alvarez A (2015) "mission planning and decision support for underwater glider networks: a sampling on-demand approach,". Sensors 16(1):28

    Article  Google Scholar 

  • Frolov S, Garau B, Bellingham J (2014) Can we do better than the grid survey: optimal synoptic surveys in presence of variable uncertainty and decorrelation scales. Oceans, Journal of Geophysical Research

    Google Scholar 

  • Gangopadhyay A, Robinson AR, Haley PJH, Leslie WG, Lozano CJ, Bisagni JJ, Yu Z (2013) Feature-oriented regional modeling and simulations in the Gulf of Maine and Georged Bank. Cont Shelf Res 23(3–4):317–353

  • Gerstoft P (1994) Inversion of siesmoacoustic data using genetic algorithms and a posteriori probability distributions. J Acoust Soc Am 95(2):770–782

    Article  Google Scholar 

  • Gerstoft P, Gingras DF (1996) Parameter estimation using multifrequency range-dependent acoustic data in shallow water. J Acoust Soc Am 99(5):2839–2850

    Article  Google Scholar 

  • Gerstoft P, Hodgkiss WS, Kuperman WA, Song HC (2003) Phenomenological and global optimization inversion. IEEE Journal of Ocean Engineering 28(3):342–354

    Article  Google Scholar 

  • Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Boston

    Google Scholar 

  • Haley PJ, Lermusiaux PFJ, Robinson AR, Leslie WG, Logutov O, Cossarini G, Liang XS, Moreno P, Ramp SR, Doyle JD, Bellingham J, Chavez F, Johnston S, (2009) Forecasting and Reanalysis in the Monterey Bay/California Current Region for the Autonomous Ocean Sampling Network-II Experiment. Special issue on AOSN-II, Deep Sea Research, Part II. ISSN 0967–0645, doi: 10.1016/j.dsr2.2008.08.010

  • Haley PJH, Lermusiaux PFJ (2010) Multiscale two-way embedding schemes for free-surface primitive-equations in the multidisciplinary simulation, estimation and assimilation system. Ocean Dyn 60:1497–1537

    Article  Google Scholar 

  • Haley PJ Jr, Agarwal A, Lermusiaux PFJ (2015) Optimizing velocities and transports for complex coastal regions and archipelagos. Ocean Modeling 89:1–28. doi:10.1016/j.ocemod.2015.02.005

    Article  Google Scholar 

  • Hamill TM, Yang F, Cardinali C, Majumdar SJ (2013) Impact of targeted winter storm reconnaissance Dropwindsonde data on Midlatitude numerical weather predictions. Mon Weather Rev 141:2058–2065

    Article  Google Scholar 

  • Heaney KD, Duda TF (2006) Non-linear optimization of multi-vehicle ocean sampling networks for cost-effective ocean prediction systems. In: OCEANS’06 Asia Pacific. IEEE, Singapore

    Google Scholar 

  • Heaney KD, Gawarkiewicz G, Duda TF, Lermusiaux PFJ (2007) "Non-linear Optimization of Autonomous Undersea Vehicle Sampling Strategies for Oceanographic Data-Assimilation." Journal of Field Robotics Special Issue on "Underwater Vehicles".

  • Hoteit I, Hoar T, Gopalakrishnan G, Collins N, Anderson J, Cornuelle BD, Köhl A, Heimbach P (2013) A MITgcm/DART ensemble analysis and prediction system with application to the Gulf of Mexico. Dynamics of Atmospheres and Oceans 63:1–23

    Article  Google Scholar 

  • Leonard NE, Paley DA, Lekien F, Sepulchre R, Fratantoni DM, Davis RE (2007) Collective motion, sensor networks, and ocean sampling. Proc IEEE 95(1):48–74

    Article  Google Scholar 

  • Lermusiaux PFJ, Robinson AR (1999) Data assimilation via error subspace statistical estimation. Part I: theory and schemes. Mon Weather Rev 127(7):1385–1407

    Article  Google Scholar 

  • Lermusiaux PFJ (2004) Application of the Error Subspace Statistical Estimation (ESSE) system to real-time error forecasting, data assimilation and adaptive sampling off the Central California Coast during AOSN-II. 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction, Seattle.

  • Lermusiaux PFJ (2006) Uncertainty Estimation and Prediction for the Interdisciplinary Ocean. Special Issue of the Journal of Computational Physics on "Uncertainty Quantification". J. Glimm and G. Karniadakis: 176–199.

  • Lermusiaux PFJ, Chiu C-S, Gawarkiewicz GG, Abbot P, Robinson AR, Miller RN, Haley PJ, Leslie WG, Majumdar SJ, Pang A, Lekien F (2006a) Quantifying uncertainties in ocean predictions. Oceanography, special issue on "advances in computational oceanography". T. Paluskiewicz and S. Harper 19:92–105

    Google Scholar 

  • Lermusiaux PFJ, Malanotte-Rizzoli P, Stammer D, Carton J, Cummings J, Moore AM (2006b) Progress and prospects of U.S. data assimilation in ocean research. In: Paluszkiewicz T, Harper S (eds) Oceanography, special issue on "Advances in Computational Oceanography". 19, 1, pp 172–183

  • Lermusiaux PFJ (2007) Adaptive Modeling, Adaptive Data Assimilation and Adaptive Sampling. Refereed invited manuscript. Special issue on "mathematical issues and challenges in data assimilation for geophysical systems: interdisciplinary perspectives". C.K.R.T. Jones and K. Ide, Eds. Physica D 230:172–196. doi:10.1016/j.physd.2007.02.014

    Article  Google Scholar 

  • Lermusiaux PFJ, Haley PJ Jr, Yilmaz NK (2007) Environmental prediction, path planning and adaptive sampling: sensing and modeling for Efficient Ocean monitoring, management and pollution control. Sea Technology 48(9):35–38

    Google Scholar 

  • Lermusiaux PFJ, Haley PJ Jr, Leslie WG, Agarwal A, Logutov O, Burton LJ (2011) Multiscale physical and biological dynamics in the Philippines archipelago: predictions and processes. Oceanography PhilEx Issue 24(1):70–89. doi:10.5670/oceanog.2011.05

    Article  Google Scholar 

  • Lermusiaux PFJ, Lolla T, Haley PJ Jr, Yigit K, Ueckermann MP, Sondergaard T, Leslie WG (2016) Science of autonomy: time-optimal path planning and adaptive sampling for swarms of ocean vehicles. In: Curtin T (ed) Springer handbook of ocean engineering: autonomous ocean vehicles, subsystems and control, Chapter 21. pp. 481–498. doi:10.1007/978-3-319-16649-0_21

  • Lin Y-T, Newhall A, Duda TF, Lermusiaux PFJ, Haley PJH (2010) Statistical merging of data sources to estimate full water-column sound speed in the New Jersey shelf shallow water 2006 experiment. IEEE J Ocean Eng 35(4):710–721

    Article  Google Scholar 

  • Logutov OG, Lermusiaux PFJ (2008) Inverse barotropic tidal estimation for regional ocean applications. Ocean Model 25(1–2):17–34

    Article  Google Scholar 

  • Lolla T (2012) Path planning in time dependent flows using level set methods. Master’s Thesis, Massachusetts Institute of Technology

    Book  Google Scholar 

  • Lolla T, Lermusiaux PFJ, Ueckermann MP, Haley PJ Jr (2014a) Time-optimal path planning in dynamic flows using level set equations: theory and schemes. Ocean Dyn 64(10):1373–1397. doi:10.1007/s10236-014-0757-y

  • Lolla T, Haley PJ, Lermusiaux PFJ (2014b) Time-optimal path planning in dynamic flows using level set equations: realistic applications. Ocean Dyn 64:1399–1417

    Article  Google Scholar 

  • Lolla T, Haley PJ Jr, Lermusiaux PFJ (2015) Path planning in multi-scale ocean flows: coordination and dynamic obstacles. Ocean Model 94:46–66. doi:10.1016/j.ocemod.2015.07.013

    Article  Google Scholar 

  • Lolla, T. (2016). Path Planning and Adaptive Sampling in the Coastal Ocean. Ph.D. Thesis, Massachusetts Institute of Technology, Department of Mechanical Engineering, February 2016.

  • Lorenz EN, Emanuel KA (1998) Optimal sites for supplementary weather observations: simulation with a small model. J Atmos Sci 55(3):399–414

    Article  Google Scholar 

  • Low, K. H., J. M. Dolan and P. Khosla (2013). Information-theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing. 19th International Conference on Automated Planning and Scheduling (ICAPS 2009).

  • Majumdar SJ, Bishop CH, Etherton BJ, Toth Z (2002) Adaptive sampling with the ensemble transform Kalman filter. Part II: field program implementation. Mon Weather Rev 130(5):1356–1369

    Article  Google Scholar 

  • Miettinen, K., 2012. Nonlinear multiobjective optimization (Vol. 12). Springer Science & Business Media

  • MSEAS Group, (2013). "Multidisciplinary Simulation, Estimation and Assimilation Systems (MSEAS)." from http://mseas.mit.edu/.

  • NOAA. (2006). "NGDC Coastal Relief Model." from http://www.ngdc.noaa.gov/mgg/coastal/coastal.html.

  • Ogren P, Fiorelli E, Leonard NE (2004) Cooperative control of mobile sensor networks: adaptive gradient climbing in a distributed environment. IEEE Trans Autom Control 49(8):1292–1302. doi:10.1109/TAC.2004.832203

    Article  Google Scholar 

  • Petillo S, Schmidt H, Lermusiaux PFJ, Yoerger D, Balasuriya A (2015) Autonomous and adaptive oceanographic front tracking on board autonomous underwater vehicles. In: Oceans 2015. Italy, Genoa

    Google Scholar 

  • Ramp SR, Davis RE, Leonard NE, Shulman I, Chao Y, Robinson AR, Marsden J, Lermusiaux PFJ, Fratantoni D, Paduan JD, Chavez F, Bahr FL, Liang S, Leslie W, Li Z (2009) Preparing to predict: the second Autonomous Ocean Sampling Network (AOSN-II) experiment in the Monterey Bay. Special issue on AOSN-II, Deep Sea Research, Part II 56:68–86. doi:10.1016/j.dsr2.2008.08.013

    Article  Google Scholar 

  • Ramp SR, Lermusiaux PFJ, Shulman I, Chao Y, Wolf RE, Bahr FL (2011) Oceanographic and atmospheric conditions on the continental shelf north of the Monterey Bay during August 2006. Dynamics of Atmospheres and Oceans 52:192–223. doi:10.1016/j.dynatmoce.2011.04.005

    Article  Google Scholar 

  • Robinson AR, Lermusiaux PFJ, Sloan NQ (1998) Data assimilation. The sea. K. H. Brink and a. R. Robinson, John Wiley & Sons 10:541–593

    Google Scholar 

  • Robinson AR, Sellschopp J, Warn-Varnas A, Leslie WG, Lozano CJ, Haley PJ Jr., Anderson LA, Lermusiaux PFJ (1999) The atlantic ionian stream. J Mar Syst 20:129–156.

  • Robinson AR, Sellschopp J, Leslie WG, Alvarez A, Baldasserini G, Haley PJ, Lermusiaux PFJ, Lozano CJ, Nacini E, Onken R, Stoner R, Zanasca P (2003) Forecasting synoptic transients in the Eastern Ligurian Sea. In: Bovio E, Tyce R, Schmidt H (eds) Rapid Environmental Assessment. SACLANTCEN Conference Proceedings Series CP-46, Saclantcen, La Spezia, Italy.

  • Roy N, Choi H-L, Gombos D, Hansen J, How J, Park S (2007) Adaptive observation strategies for forecast error minimization. In: Shi Y, van Albada GD, Dongarra J, Sloot PM (eds) Computational Science - ICCS 2007, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, vol. 4487, pp. 1138–1146. doi:10.1007/978-3-540-72584-8149

  • Schofield O, Glenn S, Orcutt J, Arrott M, Meisinger M, Gangopadhyay A, Brown W, Signell R, Moline M, Chao Y, Chien S, Thompson D, Balasuriya A, Lermusiaux PFJ, Oliver M (2010) Automated sensor networks to advance ocean science. Eos, Transactions American Geophysical Union 91(39):345–346

  • Sloan NQ (1996). Process Studies and Data-Driven Simulations in the Middle Atlantic Bight. PhD, Harvard University.

  • Smith WHF, Sandwell DT (1994) Bathymetric prediction from dense satellite altimetry and sparse shipboard bathymetry. J Geophysical Review 99:21803–21824

    Article  Google Scholar 

  • Subramani DN, Lolla T, Haley PJ Jr., Lermusiaux PFJ (2015) A stochastic optimization method for energy-based path planning. In: Ravela, S., Sandu, A. (Eds.), DyDESS 2014. Vol. 8964 of LNCS. Springer, pp. 1–12.

  • Subramani DN, Lermusiaux PFJ (2016) Energy-optimal path planning by stochastic dynamically orthogonal level-set optimization. Ocean Modeling 100:57–77. doi:10.1016/j.ocemod.2016.01.006

    Article  Google Scholar 

  • Tang D, Moum JN, Lynch JF, Abbot P, Chapman R, Dahl PH, Duda TF, Gawarkiewicz G, Glenn S, Goff JA, Graber H, Kemp J, Maffei A, Nash JD, Newhall A (2007) Shallow water ‘06: a joint acoustic propagation/nonlinear internal wave physics experiment. Oceanography 20(4):156–167

    Article  Google Scholar 

  • Toth Z, Kalnay E (1997) Ensemble forecasting at NCEP and the breeding method. Mon Weather Rev 125(12):3297–3319

    Article  Google Scholar 

  • Wang D, Lermusiaux PFJ, Haley PJH, Eickstedt D, Leslie WG, Schmidt H (2009) "Acoustically Focused Adaptive Sampling and On-board Routing for Marine Rapid Environmental Assessment." Journal of Marine Systems(Special issue “Coastal Processes: Challenges for Monitoring and Prediction”). "WHOI Shallow Water Experiment." from http://acoustics.whoi.edu/sw06/

  • Xu J, Lermusiaux PFJ, Haley PJ Jr., Leslie WG, Logutov OG (2008) Spatial and temporal variations in acoustic propagation during the PLUSNet-07 exercise in Dabob Bay. Acoustical society of America, Proceedings of meetings on acoustics (POMA). 155th Meeting, vol. 4. p 11. doi: 10.1121/1.2988093

  • Yilmaz N (2005) Path planning of autonomous underwater vehicles for adaptive sampling. PhD PhD, Massacheussetts Institute of Technology

    Google Scholar 

  • Yilmaz NK, Evangelinos C, Lermusiaux PFJ, Patrikalakis N (2008) Path planning of autonomous underwater vehicles for adaptive sampling using mixed integer linear programming. IEEE Transactions, Journal of Oceanic Engineering 33(4):522–537. doi:10.1109/JOE.2008.2002105

    Article  Google Scholar 

  • Zhang F, Fratantoni DM, Paley DA, Lund JM, Leonard NE (2007) Control of coordinated patterns for ocean sampling. Int J Control 80(7):1186–1199

    Article  Google Scholar 

  • Zhang Y, Godin MA, Bellingham JG, Ryan JP (2012) Using an autonomous underwater vehicle to track a coastal upwelling front. Oceanic Engineering, IEEE Journal of 37(3):338–347

    Article  Google Scholar 

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Acknowledgments

We are very thankful to W.G. Leslie for his help with the merging of atmospheric forcing fields used in the ocean simulations, to G. Gawarkiewicz and P. Abbot for their AWACS-SW06 ocean data, and to M. Taylor and J. Hare for their NMFS survey data. We also thank J. Evans, S. Glenn, and J. Wilkin for their real-time WRF atmospheric fluxes and the FNMOC teams for their own products. This work was supported in part by a Space and Naval Warfare Center (SPAWAR) SBIR program. PFJL and PJH are also grateful to the Office of Naval Research for partial support under grants N00014-14-1-0476 (Science of Autonomy LEARNS) and N00014-12-1-0944 (ONR6.2) to the Massachusetts Institute of Technology and N00014-11-1-0701 (MURI-IODA) to Woods Hole Oceanographic Institution. TFD’s contribution was funded by the SBIR and grant N00014- 11-1-0701 (MURI-IODA).

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Authors and Affiliations

  1. Fairfax, OASIS Inc., 11006 Clara Barton Dr. Fairfax Station, Richmond, VA, 22039, USA

    Kevin D. Heaney

  2. Massachusetts Institute of Technology, Cambridge, MA , USA

    Pierre F. J. Lermusiaux & Patrick J. Haley Jr

  3. Applied Ocean Physics and Engineering Department, Woods Hole Oceanographic Institution, MS 11, Woods Hole, MA , 02543, USA

    Timothy F. Duda

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  1. Kevin D. Heaney
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  2. Pierre F. J. Lermusiaux
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  3. Timothy F. Duda
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  4. Patrick J. Haley Jr
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Correspondence to Kevin D. Heaney or Pierre F. J. Lermusiaux.

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Heaney, K.D., Lermusiaux, P.F., Duda, T.F. et al. Validation of genetic algorithm-based optimal sampling for ocean data assimilation. Ocean Dynamics 66, 1209–1229 (2016). https://doi.org/10.1007/s10236-016-0976-5

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