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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
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
Baker NL, Daley R (2000) Observation and background adjoint sensitivity in the adaptive observation targeting problem. Q J R Meteorologic Soc 126:1431–1454
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
Balmaseda MA, Anderson D, Vidard A (2007) Impact of argo on analyses of the global ocean. Geophys Res Lett 34. doi:10.1029/2007GL030452
Barth NH (1992) Oceanographic experiment design II: genetic algorithms. J Atmos Ocean Technol 9:434–443
Berry P, Marshall J (1989) Ocean modelling studies in support of altimetry. Dyn Atmos Oceans 13:269–300
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
Bishop CH, Reynolds CA, Tippett MK (2003) Optimization of the fixed global observing network in a simple model. J Atmos Sci 60:1471–1489
Bouttier F, Kelly G (2006) Observing-system experiments in the ECMWF 4D-Var data assimilation system. Q J R Meteorologic Soc 127:1469–1488
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
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
Cardinali C, Pezzulli S, Andersson E (2004) Influence-matrix diagnostic of a data assimilation system. Q J R Meterologic Soc 130:2767–2786
Chambers DP, Tapley DB, Stewart RH (1999) Anomalous warming in the Indian Ocean coincident with El Niño. J Geophys Res 104:3035–3047
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
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
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
Evensen G (2003) The ensemble Kalman filter: theoretical formulation and practical implementation. Ocean Dyn 53:343–367
Feng M, Meyers GA, Wijffels SE (2001) Interannual upper ocean variability in the tropical Indian Ocean. Geophys Res Lett 28:4151–4154
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
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
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
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
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
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
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)
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
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
Houtekamer P, Derome J (1995) Methods for ensemble prediction. Mon Weather Rev 123:2181–2196
Khare SP, Anderson JL (2006) An examination of ensemble filters based adaptive observation methodologies. Tellus 58A:179–195
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
Kurapov AL, Egbert GD, Allen JS, Miller RN (2009) Representer-based analyses in the coastal upwelling system. Dyn Atmos Oceans 48:198–218
Langland RH (2005) Issues in targeted observations. Q J R Meteorologic Soc 131:3409–3425
Langland RH, Baker NL (2004) Estimation of observation impact using the NRL atmospheric variational data assimilation adjoint system. Tellus 56A:189–201
Masumoto Y, Meyers GA (1998) Forced Rossby waves in the southern tropical Indian Ocean. J Geophys Res 103:27589–27602
McPhaden MJ et al (1998) The tropical ocean global atmosphere (TOGA) observing system: a decade of progress. J Geophys Res 103:14169–14240
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
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
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
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
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
O’Kane TJ, Naughton M, Xiao Y (2008) AGREPS: the Australian global and regional ensemble prediction system. ANZIAM J 50:C308–C321
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
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
Oke PR, Brassington GB, Griffin DA, Schiller A (2008) The Bluelink ocean data assimilation system (BODAS). Ocean Model 21:46–70
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
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
Palmer TN, Gelaro R, Barkmeijer J, Buizza R (1998) Singular vectors, metrics, and adaptive observations. J Atmos Sci 55:633–653
Rabier F, Courtier P, Pailleuz J, Hollingsworth A (1996) Sensitivity of forecast errors to initial conditions. Q J R Meteorologic Soc 122:121–150
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
Rao SA, Behera SK (2005) Subsurface influence on SST in the tropical Indian Ocean: structure and interannual variability. Dyn Atmos Oceans 39:103–135
Sakov P, Oke PR (2008) Objective array design: application to the tropical Indian Ocean. J Atmos Ocean Technol 25:794–807
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
Schott FA, McCreary JP (2001) The monsoon circulation of the Indian Ocean. Prog Oceanogr 51:1–123
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
Snyder C, Joly A (1998) Development of perturbations within a growing baroclinic wave. Q J R Meteorologic Soc 124:1961–1983
Tippett MK, Anderson JL, Bishop CH, Hamill TM, Whitaker JS (2003) Ensemble square root filters. Mon Weather Rev 131:1485–1490
Toth Z, Kalnay E (1997) Ensemble forecasting at NCEP and the breeding method. Mon Weather Rev 125:3297–3319
Tracton M, Kalnay E (1993) Operational ensemble prediction at national meteorological center: practical aspects. Weather Forecast 8:379–398
Tremolet Y (2008) Computation of observation sensitivity and observation impact in incremental variational data assimilation. Tellus 60:964–978
Vecchi GA, Harrison MJ (2007) An observing system simulation experiment for the Indian Ocean. J Climate 20:3300–3319
Vidard A, Anderson DLT, Balmaseda M (2007) Impact of ocean observation systems on ocean analysis and seasonal forecasts. Mon Weather Rev 135:409–429
Wang X, Bishop CH (2003) A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J Atmos Sci 60:1140–1158
Wei M, Frederiksen JS (2004) Error growth and dynamical vectors during southern hemisphere blocking. Nonlinear Process Geophys 11:99–118
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
Wijffels SE, Meyers GA (2004) An intersection of oceanic waveguides: variability in the Indonesian throughflow region. J Phys Oceanogr 34:1232–1253
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
Corresponding author
Editor information
Editors and Affiliations
Rights 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
DOI: https://doi.org/10.1007/978-94-007-0332-2_5
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-0331-5
Online ISBN: 978-94-007-0332-2
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)