Application of similarity theory to forecasting the mixed-layer depth of the ocean
Abstract
The thermal structure of the ocean, especially the uppermost mixed
layer, greatly affects sonar ranges. In this paper, similarity theory
is applied to the problem of forecasting the depth of the mixed layer
during the warm season, assuming the controlling processes are secular,
non-advective , and non-divergent. The resulting forecast method consists
mainly of two equations. Parameters used are wind, coriolis effect, the
coefficient of thermal expansion and a measure of the excess heat within
the mixed layer. The constants in the equations were determined using
data from OWS Papa (50N, 145W). The forecast method treats both seasonal
and transitional thermoc lines . The method was tested with data from OWS
Papa and OWS November (30N, 140W). The tests apparently indicate wide
applicability of this forecast method and thus tend to corroborate the
proposal by Kitaigorodsky that the mixed- layer depth is a function of a
universal coefficient.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.Collections
Related items
Showing items related by title, author, creator and subject.
-
The use of classification trees to characterize the attrition process for Army manpower models
Purcell, Terence S. (Monterey, California. Naval Postgraduate School, 1997-09);The U.S. Army has a system of large personnel flow models to manage the soldiers. The partitioning of the soldiers into groups having common behavior is an important aspect of such models. This thesis presents Breiman's ... -
Suitability of Box-Jenkins modeling for Navy repair parts
Businger, Mark P. (Monterey, California. Naval Postgraduate School, 1996-09);A basic function in the proper management of repair part inventories is the forecasting of future demand. The Navy maintains a database of univariate demand data for its repair part inventories using a quarterly time ... -
Forecasting financial markets using neural networks: an analysis of methods and accuracy
Kutsurelis, Jason E. (Monterey, California. Naval Postgraduate School, 1998-09);This research examines andanalyzes the use of neural networks as a forecasting tool. Specifically a neural network's ability to predict future trends of Stock Market Indices is tested. Accuracy is compared against a ...