Assessing the performance of an ocean observing, analysis and forecast System for the Mid-Atlantic Bight using array modes

Andrew M. Moore, Julia Levin, Hernan G. Arango, John Wilkin

Research output: Contribution to journalArticlepeer-review


The efficacy of an ocean observing, analysis, and forecasting system for the Mid-Atlantic Bight and the Gulf of Maine is explored using the concept of array modes. The analysis-forecast system is based on a triply nested configuration of the Regional Ocean Modeling System (ROMS) in conjunction with 4-dimensional variational (4D-Var) data assimilation. The array modes identify the degrees of freedom (df) of the signal and of the noise resolved by the observations, and are used here to quantify the extent to which the existing network of platforms and instruments are able to observe the ocean across different dynamical regimes ranging from quasi-geostrophic through the mesoscale and down to the sub-mesoscale. The ocean observing system includes the U.S. National Science Foundation's Ocean Observatories Initiative Pioneer Array. In general, it is found that the df of the signal are largely associated with in situ observations from the Pioneer Array. On the other hand, a combination of satellite remote sensing and in situ observations potentially contribute to the df of the noise associated with uncertainties in the measurements. The array modes also provide information about the reduction in the expected analysis and forecast error covariance due to assimilating the observations. Here too observations from the Pioneer Array are found to significantly influence the veracity of the analyses and forecasts, and the circulation is instrumental in propagating observational information to other parts of the model domain. An approach is presented in which the array modes are used to quantify the impact of data assimilation on the expected forecast error covariance of forecasts initialized from the 4D-Var ocean state estimates. The advantage of this approach over others in common use is that it is independent of forecast error norm and circumvents the need for generating potentially large and costly ensembles.

Original languageEnglish (US)
Article number101821
JournalOcean Modelling
StatePublished - Aug 2021

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Oceanography
  • Geotechnical Engineering and Engineering Geology
  • Atmospheric Science


  • 4D-Var
  • Array modes
  • Data assimilation
  • Forecast error covariance
  • Mid-Atlantic Bight
  • Pioneer Array


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