Fishery management regulates size and species-specific fishing mortality to optimize biological production from the fish populations and economic production from the fishery. Fishery management is similar to management in industries and in natural resources where the goals of management are intended to optimize outputs relative to inputs. However, the management of fish populations is among the most difficult. The difficulties arise because (a) the dynamics of the natural production system are extremely complicated; involving an infinitude of variables and interacting natural systems and (b) the size-and species-specific fishing mortality (i.e. system control) is difficult to measure, calibrate, and deploy. Despite the difficulties, it is believed that significant advances can be made by employing a fishery management system that involves knowing the short-term (daily to weekly) variability in the structures of environmental and fish fields. We need new information systems that bring together existing critical technologies and thereby place fishery management in a total-systems feedback-control context. Such a system would monitor the state of the structure of all stocks simultaneously in near real-time, be adaptive to the evolving fishery and consider the effects of the environment and economics. To do this the system would need to (a) employ new in situ and remote sensors in innovative ways, (b) develop new data streams to support the development of new information, (c) employ modern modeling, information and knowledge-base technology to process the diverse information and (d) generate management advice and fishing strategies that would optimize the production of fish.

The Advanced Fisheries Management Information System (AFMIS), built through a collaboration of Harvard University and the Center for Marine Science and Technology at the University of Massachusetts at Dartmouth, is intended to apply state-of-the-art multidisciplinary and computational capabilities to operational fisheries management. The system development concept is aimed toward: 1) utilizing information on the “state” of ocean physics, biology, and chemistry; the assessment of spatially-resolved fish-stock population dynamics and the temporal-spatial deployment of fishing effort to be used in fishing and in the operational management of fish stocks; and, 2) forecasting and understanding physical and biological conditions leading to recruitment variability. Systems components are being developed in the context of using the Harvard Ocean Prediction System to support or otherwise interact with the: 1) synthesis and analysis of very large data sets; 2) building of a multidisciplinary multiscale model (coupled ocean physics/N-P-Z/fish dynamics/management models) appropriate for the northwest Atlantic shelf, particularly Georges Bank and Massachusetts Bay; 3) the application and development of data assimilation techniques; and, 4) with an emphasis on the incorporation of remotely sensed data into the data stream.

AFMIS is designed to model a large region of the northwest Atlantic (NWA) as the deep ocean influences the slope and shelves. Several smaller domains, including the Gulf of Maine (GOM) and Georges Bank (GB) are nested within this larger domain (Figure 1). This provides a capability to zoom into these domains with higher resolution while maintaining the essential physics which are coupled to the larger domain. AFMIS will be maintained by the assimilation of a variety of real time data. Specifically this includes sea surface temperature (SST), color (SSC), and height (SSH) obtained from several space-based remote sensors (AVHRR, SeaWiFS and Topex/Poseidon). The assimilation of the variety of real-time remotely sensed data supported by in situ data will allow nowcasting and forecasting over significant periods of time.

A real-time demonstration of concept (RTDOC) nowcasting and forecasting exercise to demonstrate important aspects of the AFMIS concept by producing real time coupled forecasts of physical fields, biological and chemical fields, and fish abundance fields took place in March-May 2000. The RTDOC was designed to verify the physics, to validate the biology and chemistry but only to demonstrate the concept of forecasting the fish fields, since the fish dynamical models are at a very early stage of development. In addition, it demonstrated the integrated system concept and the implication for future coupling of a management model. This note reports on the RTDOC.

A basis is outlined for the first-guess spatial mapping of three-dimensional multivariate and multiscale geophysical fields and their dominant errors. The a priori error statistics are characterized by covariance matrices and the mapping obtained by solving a minimum-error-variance estimation problem. The size of the problem is reduced efficiently by focusing on the error subspace, here the dominant eigendecomposition of the a priori error covariance. The first estimate of this a priori error subspace is constructed in two parts. For the “observed” portions of the subspace, the covariance of the a priori missing variability is directly specified and eigendecomposed. For the “non-observed” portions, an ensemble of adjustment dynamical integrations is utilized, building the nonobserved covariances in statistical accord with the observed ones. This error subspace construction is exemplified and studied in a Middle Atlantic Bight simulation and in the eastern Mediterranean. Its use allows an accurate, global, multiscale and multivariate, three-dimensional analysis of primitive-equation fields and their errors, in real time. The a posteriori error covariance is computed and indicates complex data-variability influences. The error and variability subspaces obtained can also confirm or reveal the features of dominant variability, such as the Ierapetra Eddy in the Levantine basin.

The Littoral Ocean Observing and Predicting System (LOOPS) concept is that of a generic, versatile and portable system, applicable to multidisciplinary, multiscale generic coastal processes. The LOOPS advanced systems concept consists of: a modular, scalable structure for linking, with feedbacks, models, observational networks and data assimilation and adaptive sampling algorithms; and an efficient and robust, integrated and distributed, system software architecture and infrastructure. LOOPS applications include scientific research, coastal zone management and rapid environmental assessment for naval and civilian emergency operations. The LOOPS design is the scientific and technical conceptual basis of an interdisciplinary national littoral laboratory system. The LOOPS partners include: J.G. Bellingham (MBARI), C. Chryssostomidis (MIT), T.D. Dickey (UCSB), E. Levine (NUWC), N. Patrikalakis (MIT), D.L. Porter (JHU/APL), B.J. Rothschild (Umass-Dartmouth), H. Schmidt (MIT), K. Sherman (NMFS), D.V. Holliday (Marconi Aerospace) and D.K. Atwood (Raytheon). LOOPS objectives and accomplishments are summarized in the final section of this note.

Considering mesoscale variability in the Strait of Sicily during September 1996, the four-dimensional physical fields and their dominant variability and error covariances are estimated and studied. The methodology applied in real-time combines an intensive data survey and primitive equation dynamics based on the error subspace statistical estimation approach. A sequence of filtering and prediction problems are solved for a period of 10 days, with adaptive learning of the dominant errors. Intercomparisons with optimal interpolation fields, clear sea surface temperature images and available in situ data are utilized for qualitative and quantitative evaluations. The present estimation system is shown to be a comprehensive nonlinear and adaptive assimilation scheme, capable of providing real-time forecasts of ocean fields and associated dominant variability and error covariances. The initialization and evolution of the error subspace is explained. The dominant error eigenvectors, variance and covariance fields are illustrated and their multivariate, multiscale properties described. Five coupled features associated with the dominant variability in the Strait during August-September 1996 emerge from the dominant decomposition of the initial PE variability covariance matrix: the Adventure Bank Vortex, Maltese Channel Crest, Ionian Shelf Break Vortex, Strait of Messina Vortex, and subbasin-scale temperature and salinity fronts of the Ionian slope. From the evolution of the estimated fields and dominant predictability error covariance decompositions, several of the primitive equation processes associated with the variations of these features are revealed, decomposed and studied. In general, the estimation of the evolving dominant decompositions of the multivariate predictability error and variability covariances appears promising for ocean sciences and technology. The practical feedbacks of the present approach which include the determination of data optimals and the refinements of dynamical and measurement models are considered.

This paper describes some preliminary results of the cooperative effort between SACLANT Undersea Research Centre and Harvard University in the development of a regional descriptive and predictive capability for the Strait of Sicily. The aims of the work have been to: 1. determine and describe the underlying dynamics of the region; and, 2. rapidly assess synoptic oceanographic conditions through measurements and modeling. Based on the 1994-1996 surveys, a picture of some semi-permanent features which occur in the Strait of Sicily is beginning to emerge. Dynamical circulation studies, with assimilated data from the surveys, indicate the presence of an Adventure Bank Vortex – ABV., Maltese Channel Crest – MCC., and Ionian Shelf Break Vortex – IBV. A schematic water mass model has been developed for the region. Results from the Rapid Response 96 real-time numerical modeling experiments are presented and evaluated. A newly developed data assimilation methodology, Error Subspace Statistical Estimation – ESSE. is introduced. The ideal Error Subspace spans and tracks the scales and processes where the dominant, most energetic, errors occur, making this methodology especially useful in real-time adaptive sampling. q1999 Elsevier Science B.V. All rights reserved.