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Oceanographic data assimilation and regression analysis
Author(s) -
Thompson Keith R.,
Dowd Michael,
Lu Youyu,
Smith Bruce
Publication year - 2000
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/(sici)1099-095x(200003/04)11:2<183::aid-env401>3.0.co;2-h
Subject(s) - data assimilation , multivariate statistics , regression , ocean tide , oceanography , meteorology , minification , data set , dimension (graph theory) , climatology , geology , environmental science , mathematics , statistics , geography , mathematical optimization , pure mathematics
A simple method is described for assimilating a set of irregularly spaced observations into a dynamically‐based model of the coastal ocean. The method can be used with complex models of high dimension and is relatively efficient and effective. It is based on the use of a simpler model to reduce, in an iterative fashion, the mean square difference between the observations and the predictions of the complex model. To illustrate the method we use it to predict tidal sea‐levels and currents in the Gulf of St. Lawrence, a semi‐enclosed sea off Canada's east coast, from sea‐levels measured by 19 coastal tide gauges. The method is shown to predict sea‐levels to within several cm, and currents to within several cm s −1 . To explain the method, we relate it to the familiar concept of nonlinear regression and the Gauss–Newton algorithm for the minimization of a multivariate function. Copyright © 2000 John Wiley & Sons, Ltd.