Skip to main content

Observing System Design and Assessment

  • Chapter
  • First Online:

Abstract

The use of models and data assimilation tools to aid the design and assessment of ocean observing systems is increasing. The most commonly used technique for evaluating the relative importance of existing observations is Observing System Experiments (OSEs), and Observing System Simulation Experiments (OSSEs). OSEs are useful for looking back, to evaluate the relative importance of existing of past observational components, while OSSEs are useful for looking forward, to evaluate the potential impact of future observational components. Other methods are useful for looking at the present, and are therefore most useful for adaptive sampling programs. These include analysis self-sensitivities, and a range of ensemble-based and adjoint-based techniques, including breeding, adjoint sensitivity, and singular vectors. In this chapter, the concepts for observing system design and assessment are introduced. A variety of different methods are then described, including examples of oceanographic applications of each method.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Argo Science Team (1998) On the design and implementation of argo: an initial plan for a global array of profiling floats. International CLIVAR Project Office Rep. 21, GODAE Rep. 5, GODAE Project Office, Melbourne, Australia, p 32

    Google Scholar 

  • Baker NL, Daley R (2000) Observation and background adjoint sensitivity in the adaptive observation targeting problem. Q J R Meteorologic Soc 126:1431–1454

    Article  Google Scholar 

  • Ballabrera-Poy J, Hackert E, Murtugudde R, Busalacchi AJ (2007) An observing system simulation experiment for an optimal moored instrument array in the tropical Indian Ocean. J Climate 20:3284–3299

    Article  Google Scholar 

  • Balmaseda MA, Anderson D, Vidard A (2007) Impact of argo on analyses of the global ocean. Geophys Res Lett 34. doi:10.1029/2007GL030452

    Google Scholar 

  • Barth NH (1992) Oceanographic experiment design II: genetic algorithms. J Atmos Ocean Technol 9:434–443

    Article  Google Scholar 

  • Berry P, Marshall J (1989) Ocean modelling studies in support of altimetry. Dyn Atmos Oceans 13:269–300

    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 

  • Bishop CH, Reynolds CA, Tippett MK (2003) Optimization of the fixed global observing network in a simple model. J Atmos Sci 60:1471–1489

    Article  Google Scholar 

  • Bouttier F, Kelly G (2006) Observing-system experiments in the ECMWF 4D-Var data assimilation system. Q J R Meteorologic Soc 127:1469–1488

    Article  Google Scholar 

  • Brassington GB, Divakaran P (2009) The theoretical impact of remotely sensed sea surface salinity observations in a multi-variate assimilation system. Ocean Model 27:70–81

    Article  Google Scholar 

  • Brassington GB, Pugh T, Spillman C, Schulz E, Beggs H, Schiller A, Oke PR (2007) BLUElink> development of operational oceanography and servicing in Australia. J Res Pract Inf Techol 39:151–164

    Google Scholar 

  • Cardinali C, Pezzulli S, Andersson E (2004) Influence-matrix diagnostic of a data assimilation system. Q J R Meterologic Soc 130:2767–2786

    Article  Google Scholar 

  • Chambers DP, Tapley DB, Stewart RH (1999) Anomalous warming in the Indian Ocean coincident with El Niño. J Geophys Res 104:3035–3047

    Article  Google Scholar 

  • Chapnik B, Desroziers G, Rabier F, Talagrand O (2006) Diagnosis and tuning of observational error statistics in a quasi operational data assimilation setting. Q J R Meteorologic Soc 132:543–565

    Article  Google Scholar 

  • CLIVAR–GOOS Indian Ocean Panel et al (2006) Understanding the role of the Indian Ocean in the climate system—implementation plan for sustained observations. WCRP Informal Rep. 5/2006, ICOP Publ. Series 100, GOOS Rep. 152, p 76

    Google Scholar 

  • Corazza M, Kalnay E, Patil D, Yang S-C, Morss R, Cai M, Szunyogh I, Hunt B, Yorke J (2003) Use of the breeding technique to estimate the structure of the analysis errors of the day. Nonlinear Process Geophys 10:233–243

    Article  Google Scholar 

  • Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367

    Article  Google Scholar 

  • Feng M, Meyers GA, Wijffels SE (2001) Interannual upper ocean variability in the tropical Indian Ocean. Geophys Res Lett 28:4151–4154

    Article  Google Scholar 

  • Fujii Y, Tsujino H, Usui N, Nakano H, Kamachi M (2008) Application of singular vector analysis to the Kuroshio large meander. J Geophys Res 113. doi:10.1029/2007JC004476

    Google Scholar 

  • Gallagher K, Sambridge M, Drijkoningen G (1991) Genetic algorithms: an evolution from Monte-Carlo methods for strongly non-linear geophysical optimization problems. Geophys Res Lett 18:2177–2180

    Article  Google Scholar 

  • Gelaro R, Buizza R, Palmer TN, Klinker E (1998) Sensitivity analysis of forecast errors and the construction of optimal perturbations using singular vectors. J Atmos Sci 55:1012–1037

    Article  Google Scholar 

  • Gelaro R, Langland RH, Rohaly GD, Rosmond TE (1999) As assessment of the singular-vector approach to targeted observing using the FASTEX dataset. Q J R Meteorologic Soc 125:3299–3327

    Article  Google Scholar 

  • Guinehut S, Le Traon P-Y, Larnicol G, Phillips S (2004) Combining argo and remote-sensing data to estimate the ocean three-dimensional temperature fields: a first approach based on simulated observations. J Mar Sys 46:85–98

    Article  Google Scholar 

  • Hackert EC, Miller RN, Busalacchi AJ (1998) An optimized design for a moored instrument array in the tropical Atlantic Ocean. J Geophys Res 103:7491–7509

    Article  Google Scholar 

  • Heimbach P et al (2010) Observational requirements for global-scale ocean climate analysis: lessons from ocean state estimation. In: Hall J, Harrison DE, Stammar D (eds) Proceedings of OceanObs’09: sustained ocean observations and information for society, vol 2. ESA Publication WPP-306, Venice, Italy, 21–25 Sept 2009 (submitted)

    Google Scholar 

  • Hernandez F, Le Traon P-Y, Barth N (1995) Optimizing a drifter cast strategy with a genetic algorithm. J Atmos Ocean Technol 12:330–345

    Article  Google Scholar 

  • Holland WR, Malanotte-Rizzoli P (1989) Assimilation of altimeter data into an ocean circulation model: space versus time resolution studies. J Phys Oceanogr 19:1507–1534

    Article  Google Scholar 

  • Houtekamer P, Derome J (1995) Methods for ensemble prediction. Mon Weather Rev 123:2181–2196

    Article  Google Scholar 

  • Khare SP, Anderson JL (2006) An examination of ensemble filters based adaptive observation methodologies. Tellus 58A:179–195

    Google Scholar 

  • Kuo TH, Zou X, Huang W (1998) The impact of global positioning system data on the prediction of an extratropical cyclone: an observing system simulation experiment. Dyn Atmos Oceans 27:439–470

    Article  Google Scholar 

  • Kurapov AL, Egbert GD, Allen JS, Miller RN (2009) Representer-based analyses in the coastal upwelling system. Dyn Atmos Oceans 48:198–218

    Article  Google Scholar 

  • Langland RH (2005) Issues in targeted observations. Q J R Meteorologic Soc 131:3409–3425

    Article  Google Scholar 

  • Langland RH, Baker NL (2004) Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus 56A:189–201

    Google Scholar 

  • Masumoto Y, Meyers GA (1998) Forced Rossby waves in the southern tropical Indian Ocean. J Geophys Res 103:27589–27602

    Article  Google Scholar 

  • McPhaden MJ et al (1998) The tropical ocean global atmosphere (TOGA) observing system: a decade of progress. J Geophys Res 103:14169–14240

    Article  Google Scholar 

  • Miller RN (1990) Tropical data assimilation experiments with simulated data: the impact of the tropical ocean, global atmosphere thermal array for the ocean. J Geophys Res 95:11461–11482

    Article  Google Scholar 

  • Moore AM, Farrell F (1993) Rapid perturbation growth on spatially and temporally varying oceanic flows determined using an adjoint method: application to the Gulf Stream. J Phys Oceanogr 23:1682–1702

    Article  Google Scholar 

  • Moore AM, Arango HG, Di Lorenzo E, Miller AJ, Cornuelle BD (2009) An adjoint sensitivity analysis of the southern California current circulation and ecosystem. J Phys Oceanogr 39:702–720

    Article  Google Scholar 

  • Murtugudde R, McCreary JP, Busalacchi AJ (2000) Oceanic processes associated with anomalous events in the Indian Ocean with relevance to 1997–1998. J Geophys Res 105:3295–3306

    Article  Google Scholar 

  • O’Kane TJ, Frederiksen JS (2008) Statistical dynamical subgrid-scale parameterizations for geophysical flows. Phys Scr 2008(T132):014033. doi:10.1088/0031-8949/2008/T132/014033

    Google Scholar 

  • O’Kane TJ, Naughton M, Xiao Y (2008) AGREPS: the Australian global and regional ensemble prediction system. ANZIAM J 50:C308–C321

    Google Scholar 

  • Oke PR, Schiller A (2007) Impact of argo, SST and altimeter data on an eddy-resolving ocean reanalysis. Geophys Res Lett 34. doi:10.1029/2007GL031549

    Google Scholar 

  • Oke PR, Schiller A, Griffin DA, Brassington GB (2005) Ensemble data assimilation for an eddy-resolving ocean model of the Australian region. Q J R Meteorologic Soc 131:3301–3311

    Article  Google Scholar 

  • Oke PR, Brassington GB, Griffin DA, Schiller A (2008) The Bluelink ocean data assimilation system (BODAS). Ocean Model 21:46–70

    Article  Google Scholar 

  • Oke PR, Balmaseda M, Benkiran M, Cummings JA, Dombrowsky E, Fujii Y, Guinehut S, Larnicol G, Le Traon P-Y, Martin MJ (2009) Observing system evaluations using GODAE systems. Oceanography 22(3):144–153

    Article  Google Scholar 

  • Oke PR, Balmaseda M, Benkiran M, Cummings JA, Dombrowsky E, Fujii Y, Guinehut S, Larnicol G, Le Traon P-Y, Martin MJ (2010) Observational requirements of GODAE Systems. In: Hall J, Harrison DE, Stammar D (eds) Proceedings of OceanObs’09: sustained ocean observations and information for society, vol 2, ESA Publication WPP-306, Venice, Italy, 21–25 Sept 2009

    Google Scholar 

  • Palmer TN, Gelaro R, Barkmeijer J, Buizza R (1998) Singular vectors, metrics, and adaptive observations. J Atmos Sci 55:633–653

    Article  Google Scholar 

  • Rabier F, Courtier P, Pailleuz J, Hollingsworth A (1996) Sensitivity of forecast errors to initial conditions. Q J R Meteorologic Soc 122:121–150

    Article  Google Scholar 

  • Rabier F, Gauthier P, Cardinali C, Langland R, Tsyrulnikov M, Lorenc A, Steinle P, Gelaro R, Koizumi K (2008) An update on THORPEX-related research in data assimilation and observing strategies. Nonlinear Process Geophys 15:81–94

    Article  Google Scholar 

  • Rao SA, Behera SK (2005) Subsurface influence on SST in the tropical Indian Ocean: structure and interannual variability. Dyn Atmos Oceans 39:103–135

    Article  Google Scholar 

  • Sakov P, Oke PR (2008) Objective array design: application to the tropical Indian Ocean. J Atmos Ocean Technol 25:794–807

    Article  Google Scholar 

  • Schiller A, Wijffels SE, Meyers GA (2004) Design requirements for an Argo float array in the Indian Ocean inferred from observing system simulation experiments. J Atmos Ocean Technol 21:1598–1620

    Article  Google Scholar 

  • Schott FA, McCreary JP (2001) The monsoon circulation of the Indian Ocean. Prog Oceanogr 51:1–123

    Article  Google Scholar 

  • Schouten WP, de Ruijter M, van Leeuwen PJ, Dijkstra HA (2002) An oceanic teleconnection between the equatorial and southern Indian Ocean. Geophys Res Lett 29:1812. doi:10.1029/2001GL014542

    Google Scholar 

  • Snyder C, Joly A (1998) Development of perturbations within a growing baroclinic wave. Q J R Meteorologic Soc 124:1961–1983

    Article  Google Scholar 

  • Tippett MK, Anderson JL, Bishop CH, Hamill TM, Whitaker JS (2003) Ensemble square root filters. Mon Weather Rev 131:1485–1490

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Tracton M, Kalnay E (1993) Operational ensemble prediction at national meteorological center: practical aspects. Weather Forecast 8:379–398

    Article  Google Scholar 

  • Tremolet Y (2008) Computation of observation sensitivity and observation impact in incremental variational data assimilation. Tellus 60:964–978

    Article  Google Scholar 

  • Vecchi GA, Harrison MJ (2007) An observing system simulation experiment for the Indian Ocean. J Climate 20:3300–3319

    Article  Google Scholar 

  • Vidard A, Anderson DLT, Balmaseda M (2007) Impact of ocean observation systems on ocean analysis and seasonal forecasts. Mon Weather Rev 135:409–429

    Article  Google Scholar 

  • Wang X, Bishop CH (2003) A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J Atmos Sci 60:1140–1158

    Article  Google Scholar 

  • Wei M, Frederiksen JS (2004) Error growth and dynamical vectors during southern hemisphere blocking. Nonlinear Process Geophys 11:99–118

    Article  Google Scholar 

  • Wei M, Toth Z, Wobus R, Zhu Y, Bishop CH, Wang X (2006) Ensemble transform Kalman filter-based ensemble perturbations in an operational global prediction system at NCEP. Tellus 58A:28–44

    Google Scholar 

  • Wijffels SE, Meyers GA (2004) An intersection of oceanic waveguides: variability in the Indonesian throughflow region. J Phys Oceanogr 34:1232–1253

    Article  Google Scholar 

Download references

Acknowledgments

Financial support for this research is provided by CSIRO, the Bureau of Meteorology, and the Royal Australian Navy as part of the Bluelink project, and the US Office of Naval Research (Grant No. N00014-07-1-0422). Satellite altimetry is provided by NASA, NOAA, ESA and CNES. Drifter data are provided by NOAA-AOML and SST observations are provided by NASA, NOAA and Remote Sensing Systems. Argo data are provided by the Coriolis and USGODAE data centres.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Peter R. Oke .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media B.V.

About this chapter

Cite this chapter

Oke, P.R., O’Kane, T.J. (2011). Observing System Design and Assessment. In: Schiller, A., Brassington, G. (eds) Operational Oceanography in the 21st Century. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0332-2_5

Download citation

Publish with us

Policies and ethics