A study to determine the relative skill of four model output statistics prediction methods using simulated data fields.
Loading...
Authors
Fatjo, Steve J.
Subjects
ANOVA
Forecast
Model output statistics prediction methods
Simulated Data Fields
Forecast
Model output statistics prediction methods
Simulated Data Fields
Advisors
Renard, R.J.
Preisendorfer, R.W.
Date of Issue
1986
Date
March 1986
Publisher
Monterey, CA; Naval Postgraduate School
Language
en_US
Abstract
This report describes the testing of four Model Output
Statistics prediction methods on simulated data fields for
the purpose of determining their relative skills in forecasting
a generic weather parameter (predictand) . 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 difference in the ability of one of
the four methods, namely the method using conditional probability,
to forecast the weather parameter. Through the use
of the Analysis of Variance (ANOVA) technique, this difference
is found to be significant with respect to chance.
Type
Thesis
Description
Series/Report No
Department
Meteorology
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
71 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
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.