Abstract
In the optimum interpolation scheme, the weights for the observations are computed by solving a set of linear equations for every grid point. As the number of observations increases particularly over data-rich regions, the matrix dimension increases and the computer time required to solve these equations to determine weights increases considerably. In order to reduce the computer time for computing the weights, Tanguay and Robert suggested schemes in which the gaussian function representing the autocorrelation function has been approximated by a second-order and also by a fourth-order Taylor series expansion. This resulted in the solution of matrices of order 4 or 9 respectively to obtain weighting functions irrespective of the number of observations used in the analysis. In the present study, the analyses of mean sea level pressure and geopotential height at 700 mbar level have been carried out for five days using the above two schemes and the regular OI scheme. The analyses are found to be similar in all the three cases suggesting that a lot of computer time could be saved without sacrificing the analysis accuracy by using the modified scheme in which the second-order approximation is utilized.
Similar content being viewed by others
References
Bratseth A M 1986 Statistical interpolation by means of successive correction;Tellus A38 439–447
Franke R 1988 Statistical interpolation by iteration;Mon. Weather Rev. 116 961–963
Gandin L S 1963 Objective analysis of meteorological fields;Gidrometeorologicheskoe Izdatepstvo Leningrad, USSR, 286
Hollingsworth A, Lorenc A C, Tracton M S, Arpe K, Cats G, Uppala S and Kallberg P 1985 The response of numerical weather prediction systems to FGGE level IIb data, Part I Analysis;Q. J. R. Meteorol. Soc. 111 1–66
Jullian P R and Thiebaux H J 1975 On some properties of correlation functions used in optimum interpolation schemes;Mon. Weather Rev. 103 605–616
Lorenc AC 1981 A global three-dimensional multivariate statistical interpolation scheme;Mon. Weather Rev. 109 701–721
McPherson R D, Bergman K H, Kistler R E, Rasch G and Gordon D S 1979 The NMC operational global data assimilation scheme;Mon. Weather Rev. 107 1445–1461
Seaman R S 1988 Some real data tests of the interpolation accuracy of Bratseth’s, successive correction method;Tellus A40 173–176
Seaman R S and Hutchinson M F 1985 Comparative real data tests of some objective analysis methods by withholding observations;Aust. Meteorol. Mag. 33 37–46
Shaw D B, Lonnberg P, Hollingsworth A and Unden P 1987 Data assimilation The 1984/85 revisions of the ECMWF mass and wind analysis;Q. J. R. Meteorol. Soc. 113 533–566
Tanguay M and Robert A 1990 An efficient optimum interpolation analysis scheme;Atmosphere-Ocean 28 365–377
Thiebaux H J 1975 Experiments with correlation representations for objective analysis;Mon. Weather Rev. 103 617–627
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Sinha, S.K., Narkhedkar, S.G. & Rajamani, S. An efficient optimum interpolation scheme for objective analysis over Indian region. Proc. Indian Acad. Sci. (Earth Planet Sci.) 101, 109–122 (1992). https://doi.org/10.1007/BF02840348
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF02840348