| > Oceanography > Issues > Archive > Volume 19, Number 1 |
2006, Oceanography 19(1):90–103, http://dx.doi.org/10.5670/oceanog.2006.93
Quantifying Uncertainties in Ocean Predictions
Authors | First Paragraph | Full Article | Citation
Authors
Pierre F.J. Lermusiaux | Harvard University, Cambridge, MA, USA
Ching-Sang Chiu | Naval Postgraduate School, Monterey, CA, USA
Glen G. Gawarkiewicz | Woods Hole Oceanographic Institution, Woods Hole, MA, USA
Phil Abbot | Ocean Acoustical Services and Instrumentation Systems, Inc., Lexington, MA, USA
Allan R. Robinson | Harvard University, Cambridge, MA, USA
Robert N. Miller | Oregon State University, Corvallis, OR, USA
Patrick J. Haley | Harvard University, Cambridge, MA, USA
Wayne G. Leslie | Harvard University, Cambridge, MA, USA
Sharanya J. Majumdar | University of Miami, Miami, FL, USA
Alex Pang | University of California, Santa Cruz, CA, USA
Francois Lekien | Princeton University, Princeton, NJ, USA
First Paragraph
A multitude of physical and biological processes occur in the ocean over a wide range of temporal and spatial scales. Many of these processes are nonlinear and highly variable, and involve interactions across several scales and oceanic disciplines. For example, sound propagation is influenced by physical and biological properties of the water column and by the seabed. From observations and conservation laws, ocean scientists formulate models that aim to explain and predict dynamics of the sea. This formulation is intricate because it is challenging to observe the ocean on a sustained basis and to transform basic laws into generic but usable models. There are imperfections in both data and model estimates. It is important to quantify such uncertainties to understand limitations and identify the research needed to increase accuracies, which will lead to fundamental progress.
Full Article
Citation
Lermusiaux, P.F.J., C-S. Chiu, G.G. Gawarkiewicz, P. Abbot, A.R. Robinson, R.N. Miller, P.J. Haley, W.G. Leslie, S.J. Majumdar, A. Pang, and F. Lekien. 2006. Quantifying uncertainties in ocean predictions. Oceanography 19(1):90–103, http://dx.doi.org/10.5670/oceanog.2006.93.