The Ensemble Kalman Filter (EnKF) is a popular data assimilation method for state-parameter estimation. Following a sequential assimilation strategy, it breaks the problem into alternating cycles of forecast and analysis steps. In the forecast step, the dynamical model is used to integrate a stochastic sample approximating the state analysis distribution (called analysis ensemble) to obtain a forecast ensemble. In the analysis step, the forecast ensemble is updated with the incoming observation using a Kalman-like correction, which is then used for the next forecast step. In realistic large-scale applications, EnKFs are implemented with limited ensembles, and often poorly known model errors statistics, leading to a crude approximation of the forecast covariance. This strongly limits the filter performance. Recently, a new EnKF was proposed in [1] following a one-step-ahead smoothing strategy (EnKF-OSA), which involves an OSA smoothing of the state between two successive analysis. At each time step, EnKF-OSA exploits the observation twice. The incoming observation is first used to smooth the ensemble at the previous time step. The resulting smoothed ensemble is then integrated forward to compute a "pseudo forecast" ensemble, which is again updated with the same observation. The idea of constraining the state with future observations is to add more information in the estimation process in order to mitigate for the sub-optimal character of EnKF-like methods. The second EnKF-OSA "forecast" is computed from the smoothed ensemble and should therefore provide an improved background. In this work, we propose a deterministic variant of the EnKF-OSA, based on the Singular Evolutive Interpolated Ensemble Kalman (SEIK) filter. The motivation behind this is to avoid the observations perturbations of the EnKF in order to improve the scheme's behavior when assimilating big data sets with small ensembles. The new SEIK-OSA scheme is implemented and its efficiency is demonstrated by performing assimilation experiments with the highly nonlinear Lorenz model and a realistic setting of the Advanced Circulation (ADCIRC) model configured for storm surge forecasting in the Gulf of Mexico during Hurricane Ike.

As Darkness Falls is a composition that explores our interaction with several aspects of darkness through the use of musical imagery. The imagery attempts to reflect the moods, feelings, and impressions of a person as he or she interacts with darkness. The non-programmatic character of the composition allows listeners to superimpose their own experiences onto the musical tapestry in order to manifest a personal connection between the listener and the music. As Darkness Falls is a composition scored for a minimum instrumentation of piccolo, 6 flutes, 3 oboes, 3 bassoons, 9 B-flat clarinets, B-flat bass clarinet, 2 E-flat alto saxophones, B-flat tenor saxophone, E-flat baritone saxophone, 4 B-flat trumpets, 4 horns in F, 3 tenor trombones, bass trombone, 2 euphoniums, 2 tubas, timpani, and 4 percussionists. The music consists of three movements (slow-slow-fast) lasting a total of approximately seventeen minutes. The duration of each of the three movements is six minutes, four and one-half minutes, and six and one-half minutes, respectively. The document also contains an analysis of the work by the composer. The analysis explores the compositional style of the work, focusing on musical aspects within each movement that were governing parameters in the compositional process.

The problem involved in this project is the composition of a series of selections for string ensemble. The series is comprised of five sections, each to parallel general achievement of the string students at the end of each year for five years. The degree of advancement one year over another is based not upon the slow student, nor the student who learns extremely fast. Nor is it graded to meet achievement of students who have been afforded a large amount of private instruction over the five years. Rather, it is based upon the growth and development, as observed by the writer, in a group of students of the Ogden City Schools. The writer believes that this would be typical of youngsters in other systems. Also, it is generally agreed upon by string music educators that a group of violins will reach a higher degree of proficiency over a given period of time than will the lower strings, viz., cellos and string basses. This fact has been kept in mind in the writing of these ensembles.

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Document accompanying a transcription for solo euphonium and large brass ensemble of three arias, "Ecco ridente in cielo," "Largo al factotum," and "A un dottor della mia sorte," from Gioacchino Rossini's opera The Barber of Seville. Includes overviews of the arias' texts and contexts, orchestrational techniques in the transcriptions, the history and definition of the term "euphonium," and the history of the large brass ensemble in the United States.

The purpose of this project has been to develop the interplay between guitar and piano in a jazz ensemble context. There are a lot of great examples of interplay with these instruments but there are also well-known issues when guitar and piano share a musical context. Examples of these issues can be rhythmic, harmonic, register of the instruments, the time it takes for changes in the music to happen or the direction of the music. The project is based on exercises for guitar and piano as well as specific exercises for guitar.    I discuss the different challenges and opportunities that present themselves throughout the progress of the project and how the exercises I constructed affect the different aspects of interplay. The discussion is partially based on the result of my exam concert. I also debate whether the exercises have a big impact on the result or if there’s other factors to consider.    The conclusion is that the exercises are less important than the discussions and reflections that this project has led to. The project shouldn’t be seen as a solution but rather a reflection that can give insight on the subject. / <p>Medverkande: </p><p>Samuel Hanes (Elgitarr), Eskil Larsson (Tenorsaxofon), David Stener (Piano), Jakob Ulmestrand (Kontrabas), Henrik Jäderberg (Trummor).</p><p>Framförda verk: </p><p>Untitled 06, Sorry Tom, Slip-Up, Untitled 02, Trippy Tilly. </p>

A principal factor contributing to hydrologic prediction uncertainty is modeling error intro- duced by the measurement and prediction of precipitation. The research presented demon- strates the necessity for using probabilistic methods to quantify hydrologic forecast uncer- tainty due to the magnitude of precipitation errors. Significant improvements have been made in precipitation estimation that have lead to greatly improved hydrologic simulations. However, advancements in the prediction of future precipitation have been marginal. This research shows that gains in forecasted precipitation accuracy have not significantly improved hydrologic forecasting accuracy. The use of forecasted precipitation, referred to as quantita- tive precipitation forecast (QPF), in hydrologic forecasting remains commonplace. Non-zero QPF is shown to improve hydrologic forecasts, but QPF duration should be limited to 6 to 12 hours for flood forecasting, particularly for fast responding watersheds. Probabilistic hydrologic forecasting captures hydrologic forecast error introduced by QPF for all forecast durations. However, public acceptance of probabilistic hydrologic forecasts is problematic. Central tendency measures from a probabilistic hydrologic forecast, such as the ensemble median or mean, have the appearance of a single-valued deterministic forecast. The research presented shows that hydrologic ensemble median and mean forecasts of river stage have smaller forecast errors than current operational methods with forecast lead-time beginning at 36-hours for fast response basins. Overall, hydrologic ensemble median and mean forecasts display smaller forecast error than current operational forecasts. / Ph. D.

I Am Not Frightened is a work for wind ensemble. This piece lasts around ten minutes and is inspired by confrontations with death and how to move past them. Musical inspiration comes from Pink Floyd, Greta Van Fleet, John Mackey, and James Stephenson. The title of the piece refers to the spoken lyrics found in Pink Floyd's “The Great Gig in the Sky” from their 1973 album The Darkside of the Moon. These lyrics come from the band asking Abby Road Studio workers while recording the album, if they are afraid of dying. A janitor named Gerry O’Driscoll provided the lyrics that inspired this piece: “And I am not frightened of dying. Any time will do, I don't mind. Why should I be frightened of dying? There's no reason for it – you've got to go some time.” This thought process of accepting that death is as much a part of life as

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