Assimilation of Hydrographic Data and Analysis of Model Bias

  • Keith Haines
Part of the NATO Science Series book series (NAIV, volume 26)


In this chapter we look at the assimilation of subsurface temperature profile data. Particular attention will be paid to covariances with salinity, and to the analysis of model bias in these fields. Up to now most subsurface data consists of temperature (T) profiles only without coincident salinity, although in the near future the ARGO float program will provide regular salinity measurements and the algorithms described here will need to be augmented. As discussed earlier in chapter Altimeter Covariances and Errors Treatment, section 1, the vast majority of T profile data from Expendable bathythermographs (XBTs) or from moorings tend to be of limited depth. These data are the main resource for ocean assimilation for seasonal forecasting activities and we shall illustrate the methods used by reference to results from the European Centre for Medium-range Weather Forecasts (ECMWF) seasonal forecasting system.


Data Assimilation Surface Heat Flux Heat Budget Seasonal Forecast Assimilation Experiment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bell, M.J., M.J. Martin, and N.K. Nichols, 2002: Assimilation of data into an ocean model with systematic errors near the equator. The Met. Office, Ocean Applications Tech. Note.No. 27, March 2001, 27 pp and Submitted to Q. J. R. Meteorol. Soc.Google Scholar
  2. Dee, D., and A. da Silva, 1998: Data assimilation in the presence of forecast bias. Q. J. R. Meteorol. Soc., 124, 269–295.CrossRefGoogle Scholar
  3. Fox, A.D., and K. Haines, 2003: Interpretation of Water Mass Transformations diagnosed from Data Assimilation. J. Phys Oceanogr. 33, 485–498.CrossRefGoogle Scholar
  4. Levitus, S., and T.P. Boyer, 1994: World Ocean Atlas 1994. Technical Report, US Dept. of Commerce, NOAA.Google Scholar
  5. Marshall, J., D. Jamous, and J. Nilsson, 1999: Reconciling ‘thermodynamic’ and ‘dynamic’ methods of computation of water-mass transformation rates. Deep Sea Res. I, 46, 545–572.CrossRefGoogle Scholar
  6. Nurser, A.J.G., R. Marsh, and R.G. Williams, 1999: Diagnosing water mass formation from air-sea fluxes and surface mixing. J. Phys. Oceanogr., 29, 1468–1487.CrossRefGoogle Scholar
  7. Reynolds, R. W., and T. M. Smith, 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7, 929–948.CrossRefGoogle Scholar
  8. Segschneider J., D.L.T. Anderson, J. Vialard, M. Balmaseda, T.N. Stockdale, A. Troccoli, and K. Haines, 2001: Initialization of seasonal forecasts assimilating sea level and temperature observations. J. Climate, 14, 4292–4307.CrossRefGoogle Scholar
  9. Speer, K.G., 1997: A note on average cross-isopycnal mixing in the North Atlantic ocean. Deep-Sea Res. I, 44, 1981–1990.CrossRefGoogle Scholar
  10. Troccoli, A., and K. Haines, 1999: Use of the Temperature-Salinity relation in a data assimilation context. J. Atmos. Ocean Tech., 16, 2011–2025.CrossRefGoogle Scholar
  11. Troccoli A., M. Balmaseda, J. Segschneider, J. Vialard, D.L.T. Anderson, K. Haines, T. Stockdale, F. Vitart, and Fox A.D., 2002: Salinity adjustments in the presence of temperature data assimilation. Mon. Weather Rev., 130, 89–102.CrossRefGoogle Scholar
  12. Walin G., 1982: On the relation between sea-surface heat flow and thermal circulation in the ocean. Tellus 34, 187–195.CrossRefGoogle Scholar
  13. Webb, D.J., A.C. Coward, B.A. de Cuevas, and C.S. Gwilliam, 1998: A multiprocessor ocean general circulation model using message passing. J Atmos Ocean Tech. 14, 175–183.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Keith Haines
    • 1
  1. 1.Environmental Systems Science CentreReading UniversityReadingUK

Personalised recommendations