Statistical Assimilation of Satellite Data: Method, Algorithms, Examples

  • O. M. Pokrovsky
Conference paper
Part of the NATO Science Series book series (NAIV, volume 26)


There were several reasons that caused an intensive development of the 4-D methods for the assimilation of remote sensing data in meteorology. One reason is that the satellite data are continuous in time, in contrast to conventional network observations carried out at prescribed standard terms. But the most part of remote sensing data arrives just between such terms. Another reason is that the remotely sensed meteorological parameters have to be derived from the solution of the ill-posed inverse problems. This last occasion is of special attention, because instead of conventional direct and, therefore, point-wise parameter measurements, in the case of remote sensing we have to deal with some functional of spatial field for this parameter. For example, in the case of atmospheric thermal remote sensing we cannot to retrieve temperature magnitudes at some vertical levels or at some spatial points, but rather averaged values related to some not fully certain weight functions. The third reason is that, actually, the operator of the inverse remote sensing problem does not maintain some constant magnitudes in spatial and temporal coordinates, but, really, it is subjected by disturbances originated from permanent changes occurred in atmospheric optical properties.


Satellite Orbit Radiance Data Height Field Radiosonde Data Meteorological Field 
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. Belyavsky, A.I., Pokrovsky, O.M., Spankuch, D., and Guldner, J. (1983) Numerical analysis of temperature and geopotential fields from satellite radiometric Measurements, Izvestiya, Acad.of Sciences, USSR, Atmos. and Ocean. Physics 19, 870–877.Google Scholar
  2. Bharucha-Reid, A.T. (1960) Elements of the Theory of Markov Processes and Their Applications, Mc-Graw Hill, N.Y..Google Scholar
  3. Kalman, R.E. and Bucy, R.S. (1961) New results in linear filtering and prediction theory, Trans. ASME, ser.D 83, 95–108.CrossRefGoogle Scholar
  4. Kuznetsov, P.I., Stratonovich, L.A. and Tikhonov, V.I. (1965) Non-Linear Transformations of Stochastic Processes, Pergamon Press, N.Y..Google Scholar
  5. Pokrovsky, O.M. (1974) An assimilation of conventional and satellite data in 3-D analysis of meteorological fields, Soviet Meteorology and Hydrology, 6, 33–39.Google Scholar
  6. Pokrovsky, O.M. (1974) An optimal 4-D assimilation of conventional and satellite data for meteorological field analysis, Soviet Meteorology and Hydrology, 8, 29–36. (Allerton Press Inc.,NY).Google Scholar
  7. Pokrovsky, O.M. and Ivanykin, E.E. (1978) Spatial analysis of temperature and height fields on the basis of data from remote sounding of the atmosphere, Z fur Meteorologie 1, 3–23.Google Scholar
  8. Pokrovsky, O.M. (1984) An Optimization of Meteorological Remote Sensing of Atmosphere from Satellites Hydrometeoizdat, Leningrad.Google Scholar
  9. Wiener, N. (1949) The Extrapolation, Interpolation and Smoothing of Stationary Time Series, J. Wiley, N.Y..Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • O. M. Pokrovsky
    • 1
  1. 1.Main Geophysical ObservatorySt.PetersburgRussia

Personalised recommendations