{"refrec":{"BRefID":61744,"RR":"<b>Eknes, M.; Evensen, G.</b> (2002). An Ensemble Kalman filter with a 1-D marine ecosystem model. <i>J. Mar. Syst. 36(1-2)</i>: 75-100. <a href=\"http://dx.doi.org/10.1016/s0924-7963(02)00134-3\" target=\"_blank\">http://dx.doi.org/10.1016/s0924-7963(02)00134-3</a>","BEntID":58376,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>J. Mar. Syst. 36(1-2)</i>: 75-100. <a href=\"https://dx.doi.org/10.1016/s0924-7963(02)00134-3\" target=\"_blank\">https://dx.doi.org/10.1016/s0924-7963(02)00134-3</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Eknes, M.; Evensen, G.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Eknes, M.; Evensen, G.","Englishabstract":"The Ensemble Kalman Filter (EnKF) has been examined in a data assimilation experiment with a one-dimensional three-component ecosystem model. The model is an extension of the zero-dimensional model developed by Evans and Parslow [Biol. Oceanogr. 3 (1985) 327.]. The purpose of this paper is to examine the possibilities of using a sequential data assimilation method for state estimation in a biological model, an approach which differs from the more traditional parameter estimation studies. The method chosen is the Ensemble Kalman Filer (EnKF), and it has been shown that this method captures the nonlinear error evolution in time and is capable of both tracking the reference solution and to provide realistic error estimates for the estimated state. 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