We discuss the concepts involved in the evaluation and quantitative verification of ocean forecasts and present two predictive skill experiments to develop and research these concepts, carried out in the North Atlantic and Mediterranean Sea in 2001 and 2002. Ocean forecasting involves complex ocean observing and prediction systems for ocean regions with multi-scale interdisciplinary dynamical processes and strong, intermittent events. Now that ocean forecasting is becoming more common, it is critically important to interpret and evaluate regional forecasts in order to establish their usefulness to the scientific and applied communities. The Assessment of Skill for Coastal Ocean Transients (ASCOT) project is a series of real-time Coastal Predictive Skill (CPSE) and Rapid Environmental Assessment (REA) experiments and simulations focused on quantitative skill evaluation, carried out by the Harvard Ocean Prediction System group in collaboration with the NATO SACLANT Undersea Research Centre. ASCOT-01 was carried out in Massachusetts Bay and the Gulf of Maine in June 2001. ASCOT-02 took place in May 2002 in the Corsican Channel near the island of Elba in the Mediterranean Sea. Results from the ASCOT exercises highlight the dual use of data for skill evaluation and assimilation, real-time adaptive sampling and skill optimization and present both real-time and a posteriori evaluations of predictive skill and predictive capability.

Combining an intensive hydrographic data survey with a numerical primitive equation model by data assimilation, the main features of dominant mesoscale to subbasin-scale variability in the Strait of Sicily (Mediterranean Sea) during the summer of 1996 are estimated, revealed and described, and several hydrographic and dynamical properties of the #ow and variabilities discussed. The feature identi”cation is based on two independent real-time analyses of the variability. One analysis `subjectivelya evaluates and studies physical “eld forecasts and their variations. The other more `objectivelya estimates and forecasts the principal components of the variability. The two independent analyses are found to be in agreement and complementary. The dominant dynamical variations are revealed to be associated with “ve features: the Adventure Bank Vortex, Maltese Channel Crest, Ionian Shelfbreak Vortex, Messina Rise Vortex, and temperature and salinity fronts of the Ionian slope. These features and their variations are found to have links with the meanders of the Atlantic Ionian Stream. For each feature, the characteristic physical scales, and their deviations, are quanti”ed. The predominant circulation patterns, pathways and transformations of the modi”ed Atlantic water, Ionian water and modi”ed Levantine intermediate water, are then identi”ed and discussed. For each of these water masses, the ranges of temperature, salinity, depth, velocity and residence times, and the regional variations of these ranges, are computed. Based on the estimated “elds and variability principal components, several properties of the dynamics in the Strait are discussed. These include: general characteristics of the mesoscale anomalies; bifurcations of the Atlantic Ionian Stream; respective roles of topography, atmospheric forcings and internal dynamics; factors controlling (strengthening or weakening) the vortices identi”ed; interactions of the Messina Rise and Ionian Shelfbreak vortices; and, mesoscale dynamics and relatively complex features along the Ionian slope. For evaluation and validation of the results obtained, in situ data, satellite sea surface temperature images and trajectories of surface drifters are employed, as well as comparisons with previous studies.

Data assimilation is a novel, versatile methodology for estimating oceanic variables. The estimation of a quantity of interest via data assimilation involves the combination of observational data with the underlying dynamical principles governing the system under observation. The melding of data and dynamics is a powerful methodology which makes possible efRcient, accurate, and realistic estimations otherwise not feasible. It is providing rapid advances in important aspects of both basic ocean science and applied marine technology and operations. The following sections introduce concepts, describe purposes, present applications to regional dynamics and forecasting, overview formalism and methods, and provide a selected range of examples.

A data and dynamics driven approach to estimate, decompose, organize and analyze the evolving three-dimensional variability of ocean fields is outlined. Variability refers here to the statistics of the differences between ocean states and a reference state. In general, these statistics evolve in time and space. For a first endeavor, the variability subspace defined by the dominant eigendecomposition of a normalized form of the variability covariance is evolved. A multiscale methodology for its initialization and forecast is outlined. It combines data and primitive equation dynamics within a Monte-Carlo approach. The methodology is applied to part of a multidisciplinary experiment that occurred in Massachusetts Bay in late summer and early fall of 1998. For a 4-day time period, the three-dimensional and multivariate properties of the variability standard deviations and dominant eigenvectors are studied. Two variability patterns are discussed in detail. One relates to a displacement of the Gulf of Maine coastal current offshore from Cape Ann, with the creation of adjacent mesoscale recirculation cells. The other relates to a Bay-wide coastal upwelling mode from Barnstable Harbor to Gloucester in response to strong southerly winds. Snapshots and tendencies of physical fields and trajectories of simulated Lagrangian drifters are employed to diagnose and illustrate the use of the dominant variability covariance. The variability subspace is shown to guide the dynamical analysis of the physical fields. For the stratified conditions, it is found that strong wind events can alter the structures of the buoyancy flow and that circulation features are more variable than previously described, on multiple scales. In several locations, the factors estimated to be important include some or all of the atmospheric and surface pressure forcings, and associated Ekman transports and downwelling/upwelling processes, the Coriolis force, the pressure force, inertia and mixing.

This paper explores two general methods for incorporating volumetric uncertainty information in direct volume rendering. The goal is to produce volume rendered images that depict regions of high (or low) uncertainty in the data. The first method involves incorporating the uncertainty information directly into the volume rendering equation. The second method involves post-processing information of volume rendered images to composite uncertainty information. We present some initial findings on what mappings provide qualitatively satisfactory results and what mappings do not. Results are considered satisfactory if the user can identify regions of high or low uncertainty in the rendered image. We also discuss the advantages and disadvantages of both approaches.