ADVANCES IN SEQUENTIAL DATA ASSIMILATION AND NUMERICAL WEATHER FORECASTING: AN ENSEMBLE TRANSFORM KALMAN-BUCY FILTER, A STUDY ON CLUSTERING IN DETERMINISTIC ENSEMBLE SQUARE ROOT FILTERS, AND A TEST OF A NEW TIME STEPPING SCHEME IN AN ATMOSPHERIC MODEL

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dc.contributor.advisorKalnay, Eugeniaen_US
dc.contributor.advisorIde, Kayoen_US
dc.contributor.authorAmezcua, Javieren_US
dc.contributor.departmentAtmospheric and Oceanic Sciencesen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2012-10-10T11:11:09Z
dc.date.available2012-10-10T11:11:09Z
dc.date.issued2012en_US
dc.description.abstractThis dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn't represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion in the mean value of the function. Using statistical significance tests both at the local and field level, it is shown that the climatology of the SPEEDY model is not modified by the changed time stepping scheme; hence, no retuning of the parameterizations is required. It is found the accuracy of the medium-term forecasts is increased by using the RAW filter.en_US
dc.identifier.urihttp://hdl.handle.net/1903/12986
dc.subject.pqcontrolledAtmospheric sciencesen_US
dc.subject.pquncontrolleddata assimilationen_US
dc.subject.pquncontrolledKalman filteren_US
dc.subject.pquncontrollednumerical schemesen_US
dc.subject.pquncontrollednumerical weather forecastingen_US
dc.subject.pquncontrolledpredictabilityen_US
dc.titleADVANCES IN SEQUENTIAL DATA ASSIMILATION AND NUMERICAL WEATHER FORECASTING: AN ENSEMBLE TRANSFORM KALMAN-BUCY FILTER, A STUDY ON CLUSTERING IN DETERMINISTIC ENSEMBLE SQUARE ROOT FILTERS, AND A TEST OF A NEW TIME STEPPING SCHEME IN AN ATMOSPHERIC MODELen_US
dc.typeDissertationen_US

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