Sequential estimation and satellite data assimilation in meteorology and oceanographyThe role of dynamics in estimating the state of the atmosphere and ocean from incomplete and noisy data is discussed and the classical applications of four-dimensional data assimilation to large-scale atmospheric dynamics are presented. It is concluded that sequential updating of a forecast model with continuously incoming conventional and remote-sensing data is the most natural way of extracting the maximum amount of information from the imperfectly known dynamics, on the one hand, and the inaccurate and incomplete observations, on the other.
Document ID
19880029524
Acquisition Source
Legacy CDMS
Document Type
Conference Proceedings
Authors
Ghil, M. (California, University Los Angeles; New York University, NY, United States)