A Study to Determine the Relative Skill of Four Model Output Statistics Prediction Methods Using Simulated Data Fields.
NAVAL POSTGRADUATE SCHOOL MONTEREY CA
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This thesis concerns the testing of three MOS prediction methods exercised by the previous NPS investigations the Maximum-Probability Method II, the Multiple Linear Regression Method, and the Principal Discriminant Method, plus one additional method Discriminant Analysis Method, on statistically-derived simulated i.e., controlled predictorpredict and data sets for the purpose of determining their relative skills in forecasting a generic weather parameter predicthand. Of the four methods, three use Bayes Law of Inverse Probability to discriminate, while the other method uses conditional Probability. The simulated data sets, models and observers necessary to accomplish this goal are created according to a uniquely developed simulation design. The results indicate that there is a definite diffusing conditional probability, to forcecast the weather parameter. Through the use of Analysis of Variance ANOVA technique, this difference is found to be significant with respect to chance.
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