The Role of Real-Time Four-Dimensional Data Assimilation in the Quality Control, Interpretation, and Synthesis of Climate Data

  • A. Hollingsworth
Part of the NATO ASI Series book series (ASIC, volume 284)

Abstract

Over the next decade a vast quantity of remotely sensed data and in situ data will become available for climate studies. To gain maximum benefit from these large heterogeneous datasets, it is essential that they be quality-controlled, interpreted into directly measurable quantities, and then synthesised into a consistent description of the time-evolution of the atmosphere and ocean. Operational Numerical Weather Prediction (NWP) centres have developed considerable scientific insight and technical skill in problems of this kind. Experience in NWP has shown that the quality control and the interpretation (or inversion) procedures for remotely sensed data are considerably sharpened if all available a priori information, including current and earlier observations, are brought to bear on the interpretation of the new data. This idea is implemented in NWP centres in the form of four-dimensional assimilation systems.

Based on experience with FGGE and TOGA, it is clear that there will be a strong demand for the production of timely gridded III-a analyses of all the World Climate Research Programme (WCRP) data. The efficiency of the observing systems, and of the scientific work, will be considerably enhanced if the remotely-sensed WCRP data (preferably at level 1 or 1.5) is delivered to NWP centres in real time.

Keywords

Data Assimilation Forecast Error Numerical Weatber Prediction Assimilation System Numerical Weatber Prediction Model 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, D., A. Hollingsworth, S. Uppala, P. Woiceshyn (1989): A study of the feasibility of using sea and wind information from the ERS-1 Satellite, Part I: Quality Control. Submitted to J. Geophys. Res.Google Scholar
  2. Baede, A.P.M., S. Uppala and P. Kallberg (1987): Impact of Aircraft Wind Data on ECMWF Analyses and Forecasts during the FGGE period 8–19 November to appear in Quart. J. Roy. Meteor. Soc., July 1987.Google Scholar
  3. Baer, F. (1977): Adjustment of initial conditions required to suppress gravity oscillations in non-linear flows. Beitr. Phys. Atmos., 50, 35–366.Google Scholar
  4. Barwell, B.R., and A.C. Lorenc (1985): A study of the impact of aircraft wind observations on a large scale analysis and numerical weather prediction system. Quart. J. Roy. Meteor. Soc., 111, 103–129.CrossRefGoogle Scholar
  5. Bengtsson, L., M. Kanamitsu, P. Kallberg and S. Uppala (1982): FGGE 4-dimensional data assimilation at ECMWF. Bull. Am. Meteor. Soc., 63, 29–43.Google Scholar
  6. Bengtsson, L., J. Shukla (1988): Integration of Space and in-situ observations to study global climate change. Bull. Am. Meteor. Soc., 69, 1130–1143.CrossRefGoogle Scholar
  7. Daley, R. (1985): The analysis of synoptic scale divergence by a statistical interpolation procedure. Mon. Wea. Rev., 113, 1066–1079.CrossRefGoogle Scholar
  8. Eyre, J. (1989): Inversion of cloudy satellite sounding radiances by nonlinear optimal estimation; theory and simulation for TOVS. Submitted to Quart. J. Roy. Meteor. Soc.Google Scholar
  9. Eyre, J., A.C. Lorenc (1989): Direct use of satellite sounding radiances in numerical weather prediction. Meteor. Mag., 118, 13–16.Google Scholar
  10. Gandin, L.S. (1963): Objective analysis of meteorological fields. Translated from Russian by the Israeli Program for Scientific Translations (1965).Google Scholar
  11. Ghil, M., S. Cohn, J. Tavantzis, K. Bube and E. Isaacson (1981): Applications of estimation theory to numerical weather prediction. In Dynamical Meteorology Data Assimilation Methods, Ed. L. Bengtsson, M. Ghil, E. Källén, pub Springer, pp. 139–224.Google Scholar
  12. Hasselmann, K. (1985): Assimilation of microwave data in atmospheric and wave models. Proc. ESA Alpbach Conference on use of satellite data in wave models, pp. 47–52.Google Scholar
  13. Hollingsworth, A. (1987): Objective analysis for numerical weather prediction. Special Volume J. Met. Soc. Jap. ‘Short and Medium Range Numerical Weather Prediction’ ed. T.Matsuno, pp. 11–60.Google Scholar
  14. Hollingsworth, A., A.C. Lorenc, M.S. Tracton, K. Arpe, G. Cats, S. Uppala and P. Kallberg (1985): The response of Numerical Weather Prediction Systems to FGGE II-b Data Part I: Analyses. Quart. J. Roy. Meteor. Soc., 111, 1–66.CrossRefGoogle Scholar
  15. Hollingsworth, A, D.B. Shaw, P. Lönnberg, L. Illari, K. Arpe and A.J. Simmons (1986): Monitoring of observation quality by a data assimilation system. Mon. Wea. Rev., 114, 861–879.CrossRefGoogle Scholar
  16. Hollingsworth, A. and P. Lönnberg (1986): The statistical structure of short range forecast errors as determined from radiosonde data. Part I: The wind errors. Tellus, 38A, 111–136.CrossRefGoogle Scholar
  17. Hollingsworth, A. J. Horn and S. Uppala (1989): Verification of FGGE assimilations of the tropical wind-field: The effect of model and data bias. Mon. Wea. Rev., 117, April Issue.Google Scholar
  18. Hoskins, B.J., P.D. Sardeshmukh (1987): A diagnostic study of dynamics of the northern hemisphere winter of 1985/86. Quart. J. Roy. Meteor. Soc., 113, 759–778.CrossRefGoogle Scholar
  19. Janssen, P., P. Lionello, M. Reistad, A. Hollingsworth (1989): Hindcasts and data assimilation with the WAM model during the SEASAT period. To appear in J. Geophys. Res.Google Scholar
  20. Kallberg, P. and F. Delsol (1986): Systematic biases in cloud-track-wind data from jet stream regions. Programme on Short and Medium Range Numerical Weather Prediction Research, Tokyo pp. 15–18. WMO PSMP Rept 19, available from WMO, Geneva.Google Scholar
  21. Le Dimet, F.X. and O. Talagrand (1986): Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects. Tellus, 38a, 97–110.CrossRefGoogle Scholar
  22. Lönnberg, P. and A. Hollingsworth (1986): The statistical structure of short range forecast errors as determined from radiosonde data. Part II: Covariance of height and wind errors. Tellus, 38A, 137–161.CrossRefGoogle Scholar
  23. Lorenc, A.C. (1981): A global three-dimensional multivariate statistical interpolation scheme. Mon. Wea. Rev., 109, 701–721.CrossRefGoogle Scholar
  24. Lorenc, A.C. (1986): Analysis methods for numerical weather prediction. Quart. J. Roy. Meteor. Soc., 1177–1194.Google Scholar
  25. Machenhauer, B. (1977): On the dynamics of gravity oscillations in a shallow water model, with application to normal mode initialisation. Contrib. Atmos. Phys., 50, 253–271.Google Scholar
  26. Menke, W. (1984): Geophysical Data Analysis: Discrete Inverse Theory. Academic Press pp. 260.Google Scholar
  27. Murakami, T. (1988): Intraseasonal atmospheric teleconnection patterns during the northern hemisphere winter. J. Climate, I, 117–131.CrossRefGoogle Scholar
  28. NASA (1988a): Earth Observing System, Tech.Memo. 86129.Google Scholar
  29. NASA (1988b): Earth System Science — A closer view.Google Scholar
  30. Pierrard, M.C. (1985): Intercomparison between cloud-track-winds and radiosonde winds. In Proceedings of Fifth Meteosat User Conference, Rome, pub. European Space Agency.Google Scholar
  31. Reed, R.J., A. Hollingsworth, W.A. Heckley, F. Delsol (1988): An evaluation of the performance of the ECMWF operational forecasting system in analysing and forecasting tropical easterly Wave disturbances. Mon. Wea. Rev., 116, 824–865.CrossRefGoogle Scholar
  32. Rodgers, C.D. (1976): Retrieval of atmospheric temperature and composition from remote measurement of thermal radiation. Rev. Geophys. Space PHys., 14, 609–624.CrossRefGoogle Scholar
  33. Simmons, A.J. and B.J. Hoskins (1979): The downstream and upstream development of unstable baroclinic waves. J. Atmos. Sci., 36, 1239–1254.CrossRefGoogle Scholar
  34. Temperton, C. and D.L. Williamson (1981): Normal mode initialisation for a multilevel grid-point model. Part I: Linear Aspects. Mon. Wea. Rev., 109, 729–743.CrossRefGoogle Scholar
  35. Wergen, W. (1988): Diabatic non-linear normal mode initialisation for a spectral model. Beit. Phys. Atmosph. 61, 274–302.Google Scholar
  36. Woiceshyn, P.M., M.G. Wurtele, D.H. Boggs, L.F. McGoldrick and S. Peteherych (1986): ‘The necessity for a new parameterisation of an empirical model for wind/ocean scatterometry’. J. Geophys. Res., 91, 2273–2288.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 1989

Authors and Affiliations

  • A. Hollingsworth
    • 1
  1. 1.ECMWFReadingUK

Personalised recommendations