Skip to main content

Data Assimilation, Mesoscale Dynamics and Dynamical Forecasting

  • Chapter
Advanced Physical Oceanographic Numerical Modelling

Part of the book series: NATO ASI Series ((ASIC,volume 186))

Abstract

We first introduce the concepts and methods of optimal field estimation and data assimilation. The meteorologists example is mentioned and special considerations for oceanography discussed. The research of the Harvard group for open regions of the mid latitude ocean is overviewed including hindcasting the POLYMODE SDE data, a real time forecast in the California Current and the setting up of a ODPS in the Gulf Stream ring and meander region.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bengtsson, L., M. Ghil and E. Kallen, (1981). ‘Dynamic Meteorology: Data Assimilation Methods,’ Springer-Verlag, New York, 330 pages.

    Google Scholar 

  2. Cane, M. and R. Patton, (1984). ‘A Numerical Model for Low-Frequency Equatorial Dynamics,’ Journal of Physical Oceanography, 14, (12), 1853–1863.

    Article  Google Scholar 

  3. Carter, E.F. (1983). ‘The Statistics and Dynamics of Ocean Eddies.’ Reports in Meteorology and Oceanography, No. 18, Division of Applied Sciences, Harvard University.

    Google Scholar 

  4. Carter, E.F. and A.R. Robinson, (1985). ‘An Analysis Model for the Estimation of Oceanic Fields,’ to appear, Journal of Atmospheric and Oceanic Technology.

    Google Scholar 

  5. Demey, P. and A.R. Robinson. (1985). ‘Simulations for the Assimilation of Satellite Altimetric Data at the Oceanic Mesoscale.’

    Google Scholar 

  6. Ghil, M., S. Cohn, J. Tavantzis, K. Bube and E. Isaacson, (1981). in Dynamic Meteorology: Data Assimilation Methods, (L. Bengtsson, M. Ghil and E. Kallen, eds.), Springer-Verlag, NY, NY.

    Google Scholar 

  7. Gustafsson, N, (1981). ‘A Review of Methods for Objective Analysis,’ in Dynamic Meteorology: Data Assimilation Methods, ( L. Bengtsson, M. Ghil and E. Kallen, eds.), Springer-Verlag, NY,NY.

    Google Scholar 

  8. Haidvogel, D.B., A.R. Robinson, and E.E. Shulman. (1980). ‘The Accuracy, Efficiency and Stability of Three Numerical Models with Application to Open Ocean Problems,’ Journal of Computational Physics 34 (1), 38–70.

    Article  Google Scholar 

  9. Le Provost, C., (1978). ‘The Numerical Simulation of the Non-Linear Propagation of a Long Wave in a Channel of Constant Depth,’ Comparison of Several Methods of Finite Differences, Oceanologia, 9, 95–113.

    Google Scholar 

  10. Liebeldt, P.B., (1967). ‘An Introduction to Optimal Estimation,’ Addison-Wesley, Reading, MA, 273 pages.

    Google Scholar 

  11. Miller, R.N., A.R. Robinson and D.B. Haidvogel. (1983). ‘A Baroclinic Quasigeostrophic Open Ocean Model,’ Journal of Computational Physics. 50 (1), 38–70.

    Article  Google Scholar 

  12. Miller, R.N. and A.R. Robinson. (1984). ‘Dynamical Forecast Experiments with a Baroclinic Quasigeostrophic Open Ocean Model,’ in Proceedings of Conference on Predictability of Fluid Motions, (G. Holloway and B. West, eds.), American Institute of Physics, Proceeding No. 106, AIP, NY.

    Google Scholar 

  13. Mooers, C.N.K. and A.R. Robinson. (1984). ‘Turbulent Jets and Eddies in the California Current and Inferred Cross-Shore Transports,’ Science, 223, 51–53.

    Article  Google Scholar 

  14. Morel, P., (1981). ‘An Overview of Meteorological Data Assimilation,’ in Dynamic Meteorology: Data Assimilation Methods, ( L. Bengtsson, M. Ghil and E. Kallen, eds.), Springer-Verlag, NY, NY.

    Google Scholar 

  15. Philander, S.G.H. and R.C. Pacanowski, (1984). ‘Simulation of the Seasonal Cycle in the Tropical Atlantic Ocean,’ Geophysical Research Letter, 11, 802–804.

    Article  Google Scholar 

  16. Pinardi, N. and A.R. Robinson. (1985). ‘Local Quasigeostrophic Energy and Vorticity Analysis of Mesoscale,’ Dynamics of Atmospheres and Oceans, (To Appear).

    Google Scholar 

  17. Robinson, A.R., (1985). ‘Data Assimilation, Mesoscale Dynamics and Dynamical Forecasting,’ in WOCE/TOGA Workshop on Inverse Modeling and Data Assimilation ( R. Evans, Ed.), University of Miami, Miami, FL.

    Google Scholar 

  18. Robinson, A.R., J.A. Carton, C.N.K. Mooers, L.J. Walstad, E.F. Carter, M.M. Rienecker, J.A. Smith, and W.G. Leslie. (1984). ‘A Real Time Dynamical Forecast of Ocean Synoptic/Mesoscale Eddies,’ Nature, 309 (5971). 781–783.

    Article  Google Scholar 

  19. Robinson, A.R. and M.G. Marietta, editors. (1984). Report of the Second Annual Interim Meeting of the Seabed Working Group, Physical Oceanography Task Group (POIL), Fontainebleau, France, ‘Research, Progress, and the Mark A Box Model for Physical, Biological and Chemical Transport,’ Sandia National Laboratories Report, SAND 84–0646.

    Google Scholar 

  20. Robinson, A.R. and W.G. Leslie. (1985). ‘Estimation and Prediction of Oceanic Fields,’ Progress in Oceanography, 14, 485–510.

    Article  Google Scholar 

  21. Robinson, A.R. and M.G. Marietta, editors. (1985). Report of the Third Annual Scientific Workshop meeting of the Seabed Working Group, Physical Oceanography Task Group (POIL), Neuchatel, Switzerland, ‘Research, Progress and the Description, Modelling Simulation and Dispersal Characteristics of Potential Disposal Sites in the North Atlantic,’ Sandia National Laboratories Report, SAND 85–1729.

    Google Scholar 

  22. Robinson, A.R., J.A. Carton, N. Pinardi, C.N.K. Mooers. (1985). ‘Dynamical Forecasting and Dynamical Interpolation: an Experiment in the California Current,’ submitted.

    Google Scholar 

  23. Robinson, A.R. and L.J. Walstad. (1985). ‘Numerical Modelling of Ocean Currents and Circulation,’ Numerical Fluid Dynamics, (to appear).

    Google Scholar 

  24. Smith, J.A., C.N.K. Mooers, and A.R. Robinson. (1985). ‘Estimation of Baroclinic, Quasigeostrophic Model Amplitudes from XBT/CTD Survey Data,’ Journal of Atmospheric and Oceanic Technology, (to appear).

    Google Scholar 

  25. Swiderski, E.W., (1980). ‘On Charting Global Ocean Tides,’ Review Geophysical Space Physics, 18, 243–268.

    Article  Google Scholar 

  26. Timchenko, I.E., (1984). ‘Stochastic Modeling of Ocean Dynamics,’ Harwood, Switzerland, 311 pages.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1986 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Robinson, A.R. (1986). Data Assimilation, Mesoscale Dynamics and Dynamical Forecasting. In: O’Brien, J.J. (eds) Advanced Physical Oceanographic Numerical Modelling. NATO ASI Series, vol 186. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0627-8_25

Download citation

  • DOI: https://doi.org/10.1007/978-94-017-0627-8_25

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-8428-6

  • Online ISBN: 978-94-017-0627-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics