Search

Abstract: As part of an effort to build an integrated observation and modeling system for the New York Bight, this study explores observing strategy evaluation using a representer-based method. The representer of a single observation describes the covariance between the observed quantity and ocean state errors at all locations at any time within the assimilation window. It also describes the influence of the observation on control variable correction in a 4D variational data assimilation system. These properties hold for the combination of representers that is associated with a group of observations and functions of model variables that combines model variables (e.g. salt flux). The representer-based method is used here to identify which of a set of proposed tracks for an autonomous coastal glider is better for predicting horizontal salt flux within the Hudson Shelf Valley in a 2-day forecast period. The system is also used to compare different observation strategies. We show that a glider that traverses a regular transect influences a larger area than a continuously profiling mooring, but the mooring carries stronger influence at the observation location. The representer analysis shows how the information provided by observations extends toward the dynamically upstream and how increasing the duration of the analysis window captures more dynamical connections and expands the area of influence of the observations in data assimilation. Overall, the study demonstrates that the representer methodology can quantitatively contrast different observational strategies and determine spatial patterns and temporal extent of the influence of observations, both of which are helpful for evaluating the design of observation networks. [ABSTRACT FROM AUTHOR]

Abstract: Four-dimensional variational data assimilation (4DVAR) in the Regional Ocean Modeling System (ROMS) is used to produce a best-estimate analysis of ocean circulation in the New York Bight during spring 2006 by assimilating observations collected by a variety of instruments during an intensive field program. An incremental approach is applied in an overlapped cycling system with 3-day data assimilation window to adjust model initial conditions. The model-observation mismatch for all observed variables is reduced substantially. Comparisons between model forecast and independent observations show data assimilation improves forecast skill for about 15days for temperature and salinity, and 2–3days for velocity when the model is forced by a concatenation of successive 24-h meteorological forecasts. These time scales for forecast improvement due to data assimilation may be less in practice with real-time multi-day forecast meteorology. Tests that limit the data used to certain subsets show that assimilating satellite sea surface temperature data improves the forecast of surface and subsurface temperature, assimilating in situ temperature and salinity data from gliders improves the subsurface temperature and salinity forecast, and assimilating HF-radar surface current data improves the velocity forecast yet degrades the subsurface temperature forecast – an effect that is attributed to the lack of cross-variable covariance in the univariate background error covariance used here. During some time periods the convergence for velocity is poor as a result of the data assimilation system being unable to adjust for errors in the applied winds because surface forcing is not among the control variables. The capability of a 4DVAR data assimilation system to reduce model-observation mismatch and improve forecasts in the coastal ocean is demonstrated, and the value of accurate meteorological forcing is highlighted. [ABSTRACT FROM AUTHOR]