An evaluation of discretized conditional probability and linear regression threshold techniques in model output statistics forecasting of visibility over the North Atlantic Ocean

Abstract

This report describes the application and evaluation of four primary statistical models in the forecasting of horizontal marine visibility over selected physically homogeneous area of the North Atlantic Ocean. The main focus of this study is to propose an optimal model output statistics (MOS) approach to operationally forecast visibility at the 00-hour model initialization time and the 24-hour and 48-hour model forecast projections. The technique utilized involves the manipulation of observed visibility and Navy Operational Global Atmospheric Prediction System (NOGAPS) model output parameters. The models employ the statistical methodologies of maximum conditional probability, natural regression and minimum probable error linear regression threshold techniques. Additionally, an evaluation of the 1983 predictive arrays/equations using 1984 NOGAPS data fields and a maximum-likelihood-of-detection threshold model were accomplished. Results show that two statistical approaches, namely a maximum conditional probability strategy utilizing linear regression equation predictors and the minimum probable error threshold models, produce the best results achieved in this study.