``Ocean Observation and Prediction Systems and the Potential for Progress via Distributed Computing''
Prof. Allan R. Robinson
Harvard University, Division of Engineering and Applied Sciences
Department of Earth and Planetary Sciences
Cambridge, MA 02138
E-mail: robinson@pacific.harvard.edu

The ocean is a complex multidisciplinary fluid system in which dynamical process and phenomena occur over a range of interactive space and time scales, spanning more than nine decades. The fundamental interdisciplinary problems of ocean science, identified in general more than a half century ago, are just now feasible to research. They are being pursued via research in ecosystem dynamics, bio-geo-chemical cycles, littoral-coastal-deep sea interactions, and climate and global change dynamics. Concomitantly, novel process in maritime operations and coastal ocean management is underway. Realistic four-dimensional (three spatial dimensions and time) interdisciplinary field estimation is essential for efficient research and applications. Ocean field estimation must take into account multiple scale synoptic circulation (mesoscale, jet-scale, regional, sub-basin, and basin) and episodic and intermittent variability. The multiplicity of scale, in both space and time, require nested high-resolution observational and modeling domains with accurate sub-gridscale parameterizations. State variables to be estimated include, e.g. temperature, salinity, velocity, concentrations of nutrients and plankton, ensonification, irradiance and suspended sediments. Physical process which must be represented, in the case of the coastal ocean, include: tides, fronts, waves, currents and eddies, boundary layers, surf zone effects, estuarine processes, etc. Other oceanic regimes will include other processes as well. Remote sensing of sea surface temperature, sea surface color (chlorophyll), sea surface height from satellites which are now all flying together allow for continuous ocean monitoring with high resolution (alongtrack for SSH) all over the globe. Realistic field estimates, including real-time nowcasts and forecasts as well as simulations, are now feasible because of the advent of Ocean Observing and Prediction Systems (OOPS), in which:

  1. a set of coupled interdisciplinary models are link to,
  2. an observational network consisting of various sensors mounted on a variety of platforms,
  3. via data assimilation schemes.
Compatible multiscale nested grids for the models and observations are essential. Data assimilation methods meld various types of data with dynamical estimates to improve field estimates and provide feedback for adaptive sampling and model improvement. The effective assimilation of data helps to: control model phase error, correct model dynamical deficiencies, provide parameter estimates, etc.

The Harvard Ocean Prediction System (HOPS) (Fig. 1) is an example of an OOPS: it is generic, portable, and flexible, and is being and has been used in many regions of the world ocean. HOPS is a central component of both the advanced Littoral Ocean Observing and Prediction System (LOOPS) (Fig. 2) being developed collaboratively under the National Ocean Partnership Program, and of the prototype Advanced Fisheries Management Information System (AFMIS). Ocean science is at the threshold of a new era of progress as a result of the development of multiscale, multidisciplinary OOPS. These OOPS involve novel interdisciplinary sensor, measurement, modeling and estimation methodologies and systems engineering techniques. Efficient OOPS require distributed network systems. Distributed network-based OOPS (Fig. 3) will increase the capability to manage and operate in coastal waters for: pollution control, public safety and transportation, resource exploitation and coastal zone management, and maritime and naval operations.

Experience gained with prototype OOPS will stimulate and expand the conceptual framework for knowledge networking and the scope of computational challenges for ocean science. The general principles of OOPS system architecture (Fig. 4) will be applicable to analogous systems for field estimation in other science areas: meteorology, air-sea interactions, climate dynamics, seismology, etc. These advanced systems exemplify the research directions and needs of the ocean science community for novel and efficient distributed information systems, which can only be realized by a powerful integrated effort in concert with the computational, distributed system and engineering communities.