Model error estimation in ensemble data assimilation

A new methodology is proposed to estimate and account for systematic model error in linear filtering as well as in nonlinear ensemble based filtering. Our results extend the work of Dee and Todling (2000) on constant bias errors to time-varying model errors. In contrast to existing methodologies, the new filter can also deal with the case where no dynamical model for the systematic error is available. In the latter case, the applicability is limited by a matrix rank condition which has to be satisfied in order for the filter to exist. <br><br> The performance of the filter developed in this paper is limited by the availability and the accuracy of observations and by the variance of the stochastic model error component. The effect of these aspects on the estimation accuracy is investigated in several numerical experiments using the Lorenz (1996) model. Experimental results indicate that the availability of a dynamical model for the systematic error significantly reduces the variance of the model error estimates, but has only minor effect on the estimates of the system state. The filter is able to estimate additive model error of any type, provided that the rank condition is satisfied and that the stochastic errors and measurement errors are significantly smaller than the systematic errors. The results of this study are encouraging. However, it remains to be seen how the filter performs in more realistic applications

Examining secondary school choice processes as a predictor of adjustment after the school transition

The secondary school transition is an important moment in adolescents' lives. Taking a prospective approach, the present study examined whether educational identity regarding a secondary school choice and own and parental expectations during the last year of primary school predicted post-transition school and psychological adjustment in Dutch adolescents (N = 314, Mage = 11.58). Additionally, the study qualitatively examined the reasons adolescents gave for their school choice, and linked these reasons to exploration behavior and post-transition adjustment. Identity processes and expectations predicted adjustment. Adolescents mostly reported multiple reasons for their school choice, with educational, practical, and social aspects of secondary schools appearing most important. The number of reasons mentioned was associated with pre-transition exploration behavior

Generating the future proportion of directed sardine catch taken west of Cape Agulhas in the absence of explicit spatial management

The relationship governing the proportion of directed sardine catch simulated to be taken west of Cape Agulhas during projections underlying the development of OMP-18 is updated. The relationship now differs for each draw from the posterior distribution

The 2004 re-assessment of the South African sardine and anchovy populations to take account of revisions to earlier data and recent record abundances

Hydroacoustic surveys off the coast of South Africa over the early years of the 21st century indicated that both the sardine Sardinops sagax and anchovy Engraulis encrasicolus populations had simultaneously reached record abundances. The South African pelagic fishery is regulated using an Operational Management Procedure (OMP). The OMP in use at that time had been developed using data from the two populations prior to this rapid and substantial increase in abundances. This paper documents the revised assessments that were urgently required to provide a basis to update the OMP. These assessments resulted in a changed perception of the status and productivity of these populations. In particular, estimates of the stock-recruitment relationships and the extent of variation about them, which play a key role in evaluating risk when developing OMPs, altered substantially from estimates derived from earlier assessments

Re-considering the appropriate risk level for anchovy in OMP-13 development

The acceptable level of risk changes from one MP to the next given changes in the perceived level of productivity of a resource resulting from the inclusion of revised and new data in the underlying operating models. This is because the more or less the abundance of an unexploited resource fluctuates naturally (see, for example, the biomass distributions in the absence of catches that are implied by different operating models which are shown in Figure A.1), the more or less resilient it is likely to be to reduction to a specified level through exploitation, and hence the greater or lesser the acceptable probability that fishing reduce the resource to below that level. de Moor and Butterworth (2010) developed an objective method for determining an acceptable level of risk for a new MP that maintained comparability with that adopted in selecting the previous MP. This method was applied to obtain a revised level of risk for sardine in developing Interim OMP-13 (de Moor and Butterworth 2012b). However, the application of this method to obtain a revised level of risk for anchovy for Interim OMP-13 was not straightforward given changes (supported by analyses of updated time series of data) in the selected natural mortality values and stock-recruitment relationships from the operating model used to develop OMP-08 (de Moor and Butterworth 2012a)

Updated sardine assessment

The previous full assessment of the SA sardine resource, used to develop OMP-08, was tuned to data up to and including November 2006 (Cunningham and Butterworth 2007, with further undocumented updates). Since then 2 further years of below average recruitment have been observed in the May recruitment surveys, together with a low November 2007 survey biomass estimate. This document presents an update of the sardine assessment (posterior modes only), now taking data up to October 2008 into account. This is to obtain a better understanding of the current status of the population and assist in 2009 directed sardine fishery planning

Assessment of the South African sardine resource using data from 1984-2011: further results for a two stock hypothesis

As part of the process of updating the assessment of the South African sardine resource, a model of the sardine two mixing-stock hypothesis is being developed. This hypothesis postulates a “west” stock distributed west of Cape Agulhas and a “south” stock distributed south-east of Cape Agulhas with movement from the “west” to the “south” stock in November as recruits age to 1 year olds. de Moor and Butterworth (2012a) presented some initial results for a two mixing-stock model, but cautioned that those results may not yet have fully converged and that the lack of a Hessian prevented MCMC simulation to estimate posterior distributions for key model parameters. Work has continued on this model to try to ascertain in which areas the model may be overparameterised and which “unimportant” parameters can be fixed at their estimated values without influencing results. In this document further results for the model of a two sardine mixing-stock hypothesis are presented. The current fit is improved from that of de Moor and Butterworth (2012a) and a Hessian is estimated by ADMB. The main changes to the model are detailed below. Some ideas for further work are also listed