Bayesian Estimation. Optimal Interpolation. Statistical Linear Estimation
The purpose of data assimilation can be described as to evaluate as accurately as possible the state of the atmospheric (or oceanic) flow, using all available relevant information. Depending on the particular application that is being considered, one may want to evaluate the state of the flow at a given time, or alternatively the evolution of the flow over a given period of time. As for the available information, it essentially consists of two components:
KeywordsCovariance Function Data Assimilation Data Vector Observational Error Optimal Interpolation
Unable to display preview. Download preview PDF.
- Eliassen, A. (1954) Provisional report on calculation of spatial covariance and autocorrelation of the pressure field, Report no 5, Videnskaps-Akademiets Institutt for Vaer — Og Klimaforskning, Oslo, Norway, 12 pp., Reprinted in L. Bengtsson, M. Ghil, and E. Källén (eds.), 1981 Dynamic Meteorology. Data Assimilation Methods, Springer-Verlag, New York, USA, pp. 319–328.Google Scholar
- Gandin, L. S. (1965) Objective Analysis for Meteorological Fields (translated from the Russian), Israel Program for Scientific Translations, Jerusalem, Israel.Google Scholar
- Lorenc, A. C. (1997) Quality control, in Data Assimilation, Proceedings of Seminar, September 1996, ECMWF, Reading, pp. 251–274.Google Scholar