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Avijit Gangopadhyay, A. R. Robinson, and H. G. Arango

Abstract

This is the first part of a three-part study on the circulation, dynamics, and mesoscale forecasting of the western North Atlantic. The overall objective of this series of studies is threefold: 1) to present a methodology for deriving a dynamically balanced regional climatology that maintains the synoptic structure of the permanent fronts embedded in a mean background circulation, 2) to present a methodology for using such a regional climatology for calibrating and validating dynamical models, and 3) to use similarly derived synoptic realizations as initialization and assimilation fields for mesoscale nowcasting and forecasting.

In this paper, a data-based, kinematically balanced circulation model for the western North Atlantic is developed and described. The various multiscale synoptic and general circulation structures in this region are represented by analytical and analytical/empirical functions based on dynamical considerations and using observational datasets. These include the jet-scale currents, namely, the Gulf Stream and the deep western boundary current, the subbasin-scale recirculating gyres called the southern and the northern recirculation gyres, and the slope water gyre. The inclusion of subbasin-scale gyres as the background circulation for the energetic jet and mesoscale activity in any limited oceanic region is a new paradigm of this multiscale regional modeling study. A generalized kinematical constraint that links the multiscale structures is derived in terms of their interaction scales. For synoptic realizations, the currents and gyres are distorted from their mean state with mass conserving constraints, and mesoscale structures are added thereon. The kinematically balanced linked system is then adjusted via quasigeostrophic dynamics and a regional water-mass model to obtain three-dimensional circulation fields to be used for initialization and assimilation in primitive equation models.

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Javier Zavala-Garay, J. L. Wilkin, and H. G. Arango

Abstract

One of the many applications of data assimilation is the estimation of adequate initial conditions for model forecasts. In this work, the authors evaluate to what extent the incremental, strong-constraint, four-dimensional variational data assimilation (IS4DVAR) can improve prediction of mesoscale variability in the East Australian Current (EAC) using the Regional Ocean Modeling System (ROMS). The observations considered in the assimilation experiments are daily composites of satellite sea surface temperature (SST), 7-day average reanalysis of satellite altimeter sea level anomalies, and subsurface temperature profiles from high-resolution expendable bathythermograph (XBT). Considering all available observations for years 2001 and 2002, ROMS forecast initial conditions are generated every week by assimilating the available observations from the 7 days prior to the forecast initial time. It is shown that assimilation of surface information only [SST and sea surface height (SSH)] results in poor estimates of the true subsurface ocean state (as depicted by the XBTs) and therefore poor forecast skill of subsurface conditions. Including the XBTs in the assimilation experiments improves the ocean state estimation in the vicinity of the XBT transects. By introducing subsurface pseudo-observations (which are called synthetic CTD) based on an empirical relationship between satellite surface observations and subsurface variability, the authors find a significant improvement in ocean state estimates that leads to skillful forecasts for up to 2 weeks in the domain considered.

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A. R. Robinson, H. G. Arango, A. J. Miller, A. Warn-Varnas, P.-M. Poulain, and W. G. Leslie

Real-time operational shipboard forecasts of Iceland–Faeroe frontal variability were executed for the first time with a primitive equation model. High quality, intensive hydrographic surveys during August 1993 were used for initialization, updating, and validation of the forecasts. Vigorous and rapid synoptic events occurred over several-day timescales including a southeastward reorientation of the Iceland–Faeroe Front and the development of a strong, cold deep-sock meander. A qualitative and quantitative assessment of the skill of these forecasts shows they captured the essential features of both events. The anomaly pattern correlation coefficient and the rms error between forecast and observed fields are particularly impressive (and substantially superior to persistence) for the forecast of the cold meander.

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J. C. Muccino, H. Luo, H. G. Arango, D. Haidvogel, J. C. Levin, A. F. Bennett, B. S. Chua, G. D. Egbert, B. D. Cornuelle, A. J. Miller, E. Di Lorenzo, A. M. Moore, and E. D. Zaron

Abstract

The Inverse Ocean Modeling (IOM) System is a modular system for constructing and running weak-constraint four-dimensional variational data assimilation (W4DVAR) for any linear or nonlinear functionally smooth dynamical model and observing array. The IOM has been applied to four ocean models with widely varying characteristics. The Primitive Equations Z-coordinate-Harmonic Analysis of Tides (PEZ-HAT) and the Regional Ocean Modeling System (ROMS) are three-dimensional, primitive equations models while the Advanced Circulation model in 2D (ADCIRC-2D) and Spectral Element Ocean Model in 2D (SEOM-2D) are shallow-water models belonging to the general finite-element family. These models, in conjunction with the IOM, have been used to investigate a wide variety of scientific phenomena including tidal, mesoscale, and wind-driven circulation. In all cases, the assimilation of data using the IOM provides a better estimate of the ocean state than the model alone.

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