Experiments in forecasting atmospheric marine horizontal visibility using model output statistics with conditional probabilities of discretized parameters.

• Main Author: Karl, Michael L.
• Other Authors: Renard, R.J., Naval Postgraduate School (U.S.), Meteorology, Preisendorfer, Rudolph W.
• Format: Thesis
• Language: English
• Published: Monterey, California. Naval Postgraduate School 1984
• Subjects: model output statistics; visibility; North Pacific Ocean visibility; North Atlantic Ocean visibility; marine visibility; visibility forecasting; discretization; conditional probabilities; categorical forecasting; ocean areas; homogeneous ocean areas; thresholds; linear regression; natural regression; maximum probability; Meteorology; Pacific; Newfoundland; North Atlantic
• Online Access: https://hdl.handle.net/10945/19336

This report describes the development and application of a program to forecast important air/ocean parameters using the method (s) of model output statistics. The focus of this operationally oriented study is to forecast atmospheric marine horizontal visibility using a discrete analysis of observed visibility and the Navy's Operational Global Atmospheric Prediction System (NOGAPS) model output parameters. Three strategies (two based on maximum-probability and one based on natural-regression) are compared to two multiple linear regression methods. The primary data set is from a North Atlantic Ocean area bounded approximately by the North American coast from Norfolk, Va. to St. Johns, Newfoundland, and then eastward to about 37.5°W. Both the dependent and independent data were derived from the same basic set. New or unfamiliar concepts, in addition to the primary methodology, include the statistical division of the North Atlantic Ocean into physically homogeneous areas, two new threshold models for the application of linear regression equations, linear regression based upon a 'decision-tree' concept, functional dependence of predictors and class errors. Results show that the methodology proposed by Preisendorfer does out perform multiple linear regression. Approved for public release; distribution is unlimited. Lieutenant Commander, United States Navy http://archive.org/details/experimentsinfor1094519336