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Effective mesoscale, short-range ensemble forecasting

Show simple item record Eckel, Frederick Anthony en_US 2009-10-07T00:46:22Z 2009-10-07T00:46:22Z 2003 en_US
dc.identifier.other b50820916 en_US
dc.identifier.other 53880957 en_US
dc.identifier.other Thesis 53121 en_US
dc.description Thesis (Ph. D.)--University of Washington, 2003 en_US
dc.description.abstract This study developed and evaluated a short-range ensemble forecasting (SREF) system with the goal of producing useful forecast probability ( FP). Real-time, 0 to 48-h forecasts from four different SREF systems were compared for 129 forecast cases over the Pacific Northwest. Eight analyses from different operational forecast centers were used as initial conditions (ICs) for running the Fifth-Generation Pennsylvania State University-National Center of Atmospheric Research Mesoscale Model (MM5). Additional ICs were generated through linear combinations of the original 8 analyses, but this did not result in an increase in FP skill commensurate with the increase in ensemble size. It was also found that an ensemble made up of unequally likely members can be skillful as long as all members at least occasionally perform well.Model error is a large source of forecast uncertainty and must be accounted for to maximize SREF utility, particularly for mesoscale, sensible weather phenomena. Inclusion of model perturbations in a SREF increased dispersion toward statistical consistency, but low dispersion remained problematic. Additionally, model perturbations notably improved FP skill (both reliability and resolution), revealing the significant influence of model uncertainty. Systematic model errors (i.e., biases) should always be removed from a SREF since they are a large part of forecast error but do not contribute to forecast uncertainty. A grid-based, 2-week, running-mean bias correction was shown to improve FP skill through: (1) better reliability by adjusting the ensemble mean toward the verification's mean, and (2) better resolution by reducing unrealistic ensemble variance.Comparing the multimodel (each member uses a unique model) and the perturbed-model (each member uses a unique version of MM5) approaches for accounting for model uncertainty, it was found that a multimodel SREF exhibited greater dispersion (from more complete representation of model uncertainty) and superior performance. It was also found that smaller grid spacing leads to greater ensemble spread as smaller scales of motion are modeled. This study indicates substantial utility in current SREF systems and suggests several avenues for further improvement. en_US
dc.format.extent xiii, 224 p. en_US
dc.language.iso en_US en_US
dc.rights Copyright is held by the individual authors. en_US
dc.rights.uri en_US
dc.subject.other Theses--Atmospheric sciences en_US
dc.title Effective mesoscale, short-range ensemble forecasting en_US
dc.type Thesis en_US

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