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dc.contributor.advisorEckel, F. Anthony
dc.contributor.authorAllen, Mark S.
dc.dateSeptember 2009
dc.date.accessioned2012-08-22T15:32:25Z
dc.date.available2012-08-22T15:32:25Z
dc.date.issued2009-09
dc.identifier.urihttp://hdl.handle.net/10945/10467
dc.descriptionApproved for public release, distribution unlimiteden_US
dc.description.abstractAn ensemble prediction system (EPS) generates flow-dependent estimates of uncertainty (i.e., random error due to analysis and model errors) associated with a numerical weather prediction model to provide information critical to optimal decision making. Ambiguity, or uncertainty in the prediction of forecast uncertainty, arises due to EPS deficiencies, including finite sampling and inadequate representation of the sources of forecast uncertainty. An EPS based on a low-order dynamical system was used to investigate the behavior of ambiguity, validate two practical estimation methods against a theoretical (impractical) technique, and apply ambiguity in decision making. Ambiguity generally decreased with increasing lead time and was found to depend strongly on ensemble forecast variance and the variability of ensemble mean error. The practical estimation techniques provided reasonably accurate ambiguity estimates, although they were too low at early lead times. The theoretical ambiguity estimate added significant value when combining ambiguity with forecast uncertainty to provide a single normative decision input. Additionally, value added to secondary user criteria (e.g., minimizing repeat false alarms), was explored using the practical estimations. Repeat false alarms were significantly reduced while maintaining primary value by using ambiguity information to selectively reverse normative decisions to take protective action, which effectively redistributed negative outcomes.en_US
dc.description.urihttp://archive.org/details/ambiguityinensem1094510467
dc.format.extentxxvi, 209 p. : ill. ; 28 cm.en_US
dc.publisherMonterey, California: Naval Postgraduate Schoolen_US
dc.subject.lcshValue.en_US
dc.titleAmbiguity in ensemble forecasting: evolution, estimate validation and valueen_US
dc.contributor.departmentMeteorology
dc.subject.authorEnsemble Forecasten_US
dc.subject.authorAmbiguityen_US
dc.subject.authorUncertaintyen_US
dc.subject.authorEnsemble-of-Ensembleen_US
dc.subject.authorCalibrated Error Samplingen_US
dc.subject.authorRandomly Calibrated Resamplingen_US
dc.subject.authorOptimal Decision Makingen_US
dc.subject.authorCost-Lossen_US
dc.subject.authorUncertainty-Foldingen_US
dc.subject.authorSecondary Criteriaen_US
dc.subject.authorLorenz '96en_US
dc.subject.authorEnsemble Prediction Systems;en_US
etd.thesisdegree.namePh.D. in Meteorologyen_US
etd.thesisdegree.levelDoctoralen_US
etd.thesisdegree.disciplineMeteorologyen_US
etd.thesisdegree.grantorNaval Postgraduate School (U.S.)en_US


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