Gregoire, M., P. Brasseur and P.F.J. Lermusiaux (Guest Eds.), 2003. *The use of data assimilation in coupled hydrodynamic, ecological and bio-geo-chemical models of the ocean.* Journal of Marine Systems, 40, 1-3.

The International Lie`ge Colloquium on Ocean
Dynamics is organized annually. The topic differs
from year to year in an attempt to address, as much
as possible, recent problems and incentive new subjects
in oceanography.
Assembling a group of active and eminent scientists
from various countries and often different disciplines,
the Colloquia provide a forum for discussion
and foster a mutually beneficial exchange of information
opening on to a survey of recent discoveries,
essential mechanisms, impelling question marks and
valuable recommendations for future research.
The objective of the 2001 Colloquium was to
evaluate the progress of data assimilation methods in
marine science and, in particular, in coupled hydrodynamic,
ecological and bio-geo-chemical models of
the ocean.
The past decades have seen important advances
in the understanding and modelling of key processes
of the ocean circulation and bio-geo-chemical
cycles. The increasing capabilities of data and
models, and their combination, are allowing the
study of multidisciplinary interactions that occur
dynamically, in multiple ways, on multiscales and
with feedbacks.
The capacity of dynamical models to simulate interdisciplinary
ocean processes over specific space-
time windows and thus forecast their evolution over
predictable time scales is also conditioned upon the
availability of relevant observations to: initialise and
continually update the physical and bio-geo-chemical
sectors of the ocean state; provide relevant atmospheric
and boundary forcing; calibrate the parameterizations
of sub-grid scale processes, growth rates and
reaction rates; construct interdisciplinary and multiscale
correlation and feature models; identify and
estimate the main sources of errors in the models;
control or correct for mis-represented or neglected
processes.
The access to multivariate data sets requires the
implementation, exploitation and management of dedicated
ocean observing and prediction systems. However,
the available data are often limited and, for
instance, seldom in a form to be directly compatible
or directly inserted into the numerical models. To relate
the data to the ocean state on all scales and regions that
matter, evolving three-dimensional and multivariate
(measurement) models are becoming important.
Equally significant is the reduction of observational
requirements by design of sampling strategies via
Observation System Simulation Experiments and
adaptive sampling.
Data assimilation is a quantitative approach to
extract adequate information content from the data
and to improve the consistency between data sets and
model estimates. It is also a methodology to dynamically
interpolate between data scattered in space and
time, allowing comprehensive interpretation of multivariate
observations.
In general, the goals of data assimilation are to:
control the growth of predictability errors; correct
dynamical deficiencies; estimate model parameters,
including the forcings, initial and boundary conditions;
characterise key processes by analysis of four-
0924-7963/03/$ – see front matter D 2003 Elsevier Science B.V. All rights reserved.
doi:10.1016/S0924-7963(03)00027-7
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The use of data assimilation in coupled hydrodynamic, ecological and
bio-geo-chemical models of the ocean
Journal of Marine Systems 40-41 (2003) 1-3
dimensional fields and their statistics (balances of
terms, etc.); carry out advanced sensitivity studies
and Observation System Simulation Experiments,
and conduct efficient operations, management and
monitoring.
The theoretical framework of data assimilation
for marine sciences is now relatively well established,
routed in control theory, estimation theory or inverse
techniques, from variational to sequential approaches.
Ongoing research efforts of special importance for
interdisciplinary applications include the: stochastic
representation of processes and determination of
model and data errors; treatment of (open) boundary
conditions and strong nonlinearities; space-time,
multivariate extrapolation of limited and noisy data
and determination of measurement models; demonstration
that bio-geo-chemical models are valid
enough and of adequate structures for their deficiencies
to be controlled by data assimilation; and finally,
ability to provide accurate estimates of fields, parameters,
variabilities and errors, with large and complex
dynamical models and data sets.
Operationally, major engineering and computational
challenges for the coming years include the:
development of theoretically sound methods into
useful, practical and reliable techniques at affordable
costs; implementation of scalable, seamless and automated
systems linking observing systems, numerical
models and assimilation schemes; adequate mix of
integrated and distributed (Web-based) networks; construction
of user-friendly architectures and establishment
of standards for the description of data and
software (metadata) for efficient communication, dissemination
and management.
In addition to addressing the above items, the 33rd
Lie`ge Colloquium has offered the opportunity to:
– review the status and current progress of data
assimilation methodologies utilised in the physical,
acoustical, optical and bio-geo-chemical
scientific communities;
– demonstrate the potentials of data assimilation
systems developed for coupled physical/ecosystem
models, from scientific to management inquiries;
– examine the impact of data assimilation and
inverse modelling in improving model parameterisations;
– discuss the observability and controllability properties
of, and identify the missing gaps in current
observing and prediction systems; and
exchange the results of and the learnings from preoperational
marine exercises.
The presentations given during the Colloquium
lead to discussions on a series of topics organized
within the following sections: (1) Interdisciplinary
research progress and issues: data, models, data
assimilation criteria. (2) Observations for interdisciplinary
data assimilation. (3) Advanced fields estimation
for interdisciplinary systems. (4) Estimation of
interdisciplinary parameters and model structures. (5)
Assimilation methodologies for physical and interdisciplinary
systems. (6) Toward operational interdisciplinary
oceanography and data assimilation. A subset
of these presentations is reported in the present
Special Issue.
As was pointed out during the Colloquium, coupled
biological-physical data assimilation is in its infancy
and much can be accomplished now by the immediate
application of existing methods. Data assimilation
intimately links dynamical models and observations,
and it can play a critical role in the important area of
fundamental biological oceanographic dynamical
model development and validation over a hierarchy
of complexities. Since coupled assimilation for coupled
processes is challenging and can be complicated, care
must be exercised in understanding, modeling and
controlling errors and in performing sensitivity analyses
to establish the robustness of results. Compatible
interdisciplinary data sets are essential and data assimilation
should iteratively define data impact and data
requirements.
Based on the results presented during the Colloquium,
data assimilation is expected to enable future
marine technologies and naval operations otherwise
impossible or not feasible. Interdisciplinary predictability
research, multiscale in both space and time, is
required. State and parameter estimation via data
assimilation is central to the successful establishment
of advanced interdisciplinary ocean observing and
prediction systems which, functioning in real time,
will contribute to novel and efficient capabilities to
manage, and to operate in our oceans.
The Scientific Committee and the participants to
the 33rd Lie`ge Colloquium wish to express their
2 Preface
gratitude to the Ministe`re de l’Enseignement Supe’rieur
et de la Recherche Scientifique de la Communaute
– Francaise de Belgique, the Fonds National de
la Recherche Scientifique de Belgique (F.N.R.S.,
Belgium), the Ministe`re de l’Emploi et de la Formation
du Gouvernement Wallon, the University of
Lie`ge, the Commission of European Union, the
Scientific Committee on Oceanographic Research
(SCOR), the International Oceanographic Commission
of the UNESCO, the US Office of Naval
Research, the National Science Foundation (NSF,
USA) and the International Association for the
Physical Sciences of the Ocean (IAPSO) for their
most valuable support.