Application of ensemble-based techniques in fish stock assessment

被引:17
作者
Gronnevik, R
Evensen, G
机构
[1] Nansen Environm & Remote Sensing Ctr, N-5059 Bergen, Norway
[2] Univ Bergen, Dept Math, N-5020 Bergen, Norway
来源
SARSIA | 2001年 / 86卷 / 06期
关键词
fish stock estimation; data assimilation; ensemble Kalman Filter; ensemble Kalman Smoother;
D O I
10.1080/00364827.2001.10420490
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
A basic fish-stock assessment system requires an integrated use of a model for the time evolution of a fish stock and information about the fish stock from catch data. Typical for common fish stock assessment systems has been the use of fairly simplistic data assimilation methodologies for the integration of observations with the dynamical models. On the other hand, there has been a fast development of assimilation techniques which can be used with highly nonlinear and complex dynamical models. In this paper some of these methods, which are based on Monte Carlo formulations, will be examined with a simple fish stock model. The methods used are the ensemble Kalman filter, the ensemble Kalman smoother and the simpler ensemble smoother.
引用
收藏
页码:517 / 526
页数:10
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