• DocumentCode
    746851
  • Title

    The Long-term Ecosystem Observatory: an integrated coastal observatory

  • Author

    Schofield, Oscar ; Bergmann, Trisha ; Bissett, Paul ; Grassle, J. Frederick ; Haidvogel, Dale B. ; Kohut, Josh ; Moline, Mark ; Glenn, Scott M.

  • Author_Institution
    Coastal Ocean Obs. Lab., Rutgers Univ., New Brunswick, NJ, USA
  • Volume
    27
  • Issue
    2
  • fYear
    2002
  • fDate
    4/1/2002 12:00:00 AM
  • Firstpage
    146
  • Lastpage
    154
  • Abstract
    An integrated ocean observatory has been developed and operated in the coastal waters off the central coast of New Jersey, USA. One major goal for the Long-term Ecosystem Observatory (LEO) is to develop a real-time capability for rapid environmental assessment and physical/biological forecasting in coastal waters. To this end, observational data are collected from satellites, aircrafts, ships, fixed/relocatable moorings and autonomous underwater vehicles. The majority of the data are available in real-time allowing for adaptive sampling of episodic events and are assimilated into ocean forecast models. In this observationally rich environment, model forecast errors are dominated by uncertainties in the model physics or future boundary conditions rather than initial conditions. Therefore, ensemble forecasts with differing model parameterizations provide a unique opportunity for model refinement and validation. The system has been operated during three annual coastal predictive skill experiments from 1998 through 2000. To illustrate the capabilities of the system, case studies on coastal upwelling and small-scale biological slicks are discussed. This observatory is one part of the expanding network of ocean observatories that will form the basis of a national observation network
  • Keywords
    ecology; oceanographic techniques; real-time systems; remote sensing; water pollution measurement; Long-Term Ecosystem Observatory; New Jersey coast; coastal upwelling; coastal waters; ensemble forecasts; integrated coastal observatory; model forecast errors; national observation network; observational data collection; physical/biological forecasting; rapid environmental assessment; real-time capability; small-scale biological slicks; Aircraft; Biological system modeling; Ecosystems; Low earth orbit satellites; Marine vehicles; Observatories; Oceans; Predictive models; Sea measurements; Underwater vehicles;
  • fLanguage
    English
  • Journal_Title
    Oceanic Engineering, IEEE Journal of
  • Publisher
    ieee
  • ISSN
    0364-9059
  • Type

    jour

  • DOI
    10.1109/JOE.2002.1002469
  • Filename
    1002469