Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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.
Bharucha-Reid, A.T. (1960) Elements of the Theory of Markov Processes and Their Applications, Mc-Graw Hill, N.Y..
Kalman, R.E. and Bucy, R.S. (1961) New results in linear filtering and prediction theory, Trans. ASME, ser.D 83, 95–108.
Kuznetsov, P.I., Stratonovich, L.A. and Tikhonov, V.I. (1965) Non-Linear Transformations of Stochastic Processes, Pergamon Press, N.Y..
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.
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).
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.
Pokrovsky, O.M. (1984) An Optimization of Meteorological Remote Sensing of Atmosphere from Satellites Hydrometeoizdat, Leningrad.
Wiener, N. (1949) The Extrapolation, Interpolation and Smoothing of Stationary Time Series, J. Wiley, N.Y..
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer Science+Business Media Dordrecht
About this paper
Cite this paper
Pokrovsky, O.M. (2003). Statistical Assimilation of Satellite Data: Method, Algorithms, Examples. In: Swinbank, R., Shutyaev, V., Lahoz, W.A. (eds) Data Assimilation for the Earth System. NATO Science Series, vol 26. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-0029-1_16
Download citation
DOI: https://doi.org/10.1007/978-94-010-0029-1_16
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-1593-9
Online ISBN: 978-94-010-0029-1
eBook Packages: Springer Book Archive