Towards an integrated observation and modeling system in the New York Bight using variational methods. Part I: 4DVAR data assimilation

Abstract

Four-dimensional Variational data assimilation (4DVAR) in the Regional OceanModeling 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 improved forecast skill for about 15 days for temperature and salinity, and 2 to 3 days for velocity. Tests assimilating only certain subsets of the data indicate that assimilating satellite sea surface temperature improves the forecast of surface and subsurface temperature but worsens the salinity forecast. Assimilating in situ temperature and salinity from gliders improves the salinity forecast but has little effect on temperature. Assimilating HF-radar surface current data improves the velocity forecast by 1-2 days yet worsens the forecast of subsurface temperature. During some time periods the convergence for velocity is poor as a result of the data assimilation system being unable to reduce errors in the applied winds because surface forcing is not among the control variables. This study demonstrates the capability of 4DVAR data assimilation system to reduce model-observation mismatch and improve forecasts in the coastal ocean, and highlights the value of accurate meteorological forcing.

Remote sensing of ocean surface currents: a review of what is being observed and what is being assimilated

“…Since then, and driven by the continuous expansion of the network of HF radar systems, different data assimilation approaches have been used to assimilate HF radar currents into nonlinear, high-resolution ocean models: nudging (Lewis et al, 1998;Wilkin et al, 2005;Gopalakrishnan and Blumberg, 2012), sequential assimilation (Breivik and Saetra, 2001;Oke et al, 2002;Paduan and Shulman, 2004;Kurapov et al, 2005a;Oke et al, 2009) and 4DVAR assimilation schemes (Hoteit et al, 2009;Zhang et al, 2010;Yu et al, 2012).…”

“…The first implementation of a multivariate assimilation of multiple data sources including HF radar currents was done by Zhang et al (2010) in the New York Bight using the Regional Ocean Modeling System (ROMS) model (Haidvogel et al, 2008) and its adjoint model (Di Lorenzo et al, 2007). Their data assimilation method was an incremental strongconstrain 4DVAR ) that only adjusted the initial conditions using assimilation windows of 3 days, overlapping the data assimilation windows, advancing the beginning of the data assimilation window by 1 day.…”

“…Their data assimilation method was an incremental strongconstrain 4DVAR ) that only adjusted the initial conditions using assimilation windows of 3 days, overlapping the data assimilation windows, advancing the beginning of the data assimilation window by 1 day. Using a series of sensitivity experiments, Zhang et al (2010) revealed that the assimilation of HF radar currents in the model increased the current prediction skill of the model by 1-2 days. However, assimilation of surface currents slightly degraded the prediction skill of subsurface temperature.…”

Some considerations about coastal ocean observing systems

“…A number of studies have examined the potential gains in coastal ocean model accuracy via assimilation of HF radar surface current observations (Oke et al 2002;Kurapov et al 2003Kurapov et al , 2009Oke, Sakov, and Schulz 2009;Zhang, Wilkin, and Arango 2010). Using the HF radar data for assimilation in models does call for some special attention because of the vertical confinement of the information (e.g., Oke et al 2002), but the methodology is now well in hand.…”

Issues and progress in the prediction of ocean submesoscale features and internal waves

“…There are also a few summary papers available from the 2009 OceanObs conference [18][19][20][21]. The vital process of optimally and consistently assimilating data into models for each of these purposes is an active area of research with significant progress over the last decade [22][23][24][25], and will not be addressed further here. Instead we move to the third, fifth and sixth bullets in the list above, relating to model resolution and the barely-resolved or unresolved submesoscale processes.…”