Dokument-ID Dokumenttyp Verfasser/Autoren Herausgeber Haupttitel Abstract Auflage Verlagsort Verlag Erscheinungsjahr Seitenzahl Schriftenreihe Titel Schriftenreihe Bandzahl ISBN Quelle der Hochschulschrift Konferenzname Quelle:Titel Quelle:Jahrgang Quelle:Heftnummer Quelle:Erste Seite Quelle:Letzte Seite URN DOI Abteilungen OPUS4-35783 Wissenschaftlicher Artikel Shin, Seoleun; Reich, Sebastian; Frank, Jason Hydrostatic Hamiltonian particle-mesh (HPM) methods for atmospheric modelling We develop a hydrostatic Hamiltonian particle-mesh (HPM) method for efficient long-term numerical integration of the atmosphere. In the HPM method, the hydrostatic approximation is interpreted as a holonomic constraint for the vertical position of particles. This can be viewed as defining a set of vertically buoyant horizontal meshes, with the altitude of each mesh point determined so as to satisfy the hydrostatic balance condition and with particles modelling horizontal advection between the moving meshes. We implement the method in a vertical-slice model and evaluate its performance for the simulation of idealized linear and nonlinear orographic flow in both dry and moist environments. The HPM method is able to capture the basic features of the gravity wave to a degree of accuracy comparable with that reported in the literature. The numerical solution in the moist experiment indicates that the influence of moisture on wave characteristics is represented reasonably well and the reduction of momentum flux is in good agreement with theoretical analysis. Hoboken Wiley-Blackwell 2012 12 Quarterly journal of the Royal Meteorological Society 138 666 1388 1399 10.1002/qj.982 Institut für Mathematik OPUS4-35837 Wissenschaftlicher Artikel Bergemann, Kay; Reich, Sebastian An ensemble Kalman-Bucy filter for continuous data assimilation The ensemble Kalman filter has emerged as a promising filter algorithm for nonlinear differential equations subject to intermittent observations. In this paper, we extend the well-known Kalman-Bucy filter for linear differential equations subject to continous observations to the ensemble setting and nonlinear differential equations. The proposed filter is called the ensemble Kalman-Bucy filter and its performance is demonstrated for a simple mechanical model (Langevin dynamics) subject to incremental observations of its velocity. Stuttgart Schweizerbart 2012 7 Meteorologische Zeitschrift 21 3 213 219 10.1127/0941-2948/2012/0307 Institut für Mathematik OPUS4-36378 Wissenschaftlicher Artikel Reich, Sebastian A Gaussian-mixture ensemble transform filter We generalize the popular ensemble Kalman filter to an ensemble transform filter, in which the prior distribution can take the form of a Gaussian mixture or a Gaussian kernel density estimator. The design of the filter is based on a continuous formulation of the Bayesian filter analysis step. We call the new filter algorithm the ensemble Gaussian-mixture filter (EGMF). The EGMF is implemented for three simple test problems (Brownian dynamics in one dimension, Langevin dynamics in two dimensions and the three-dimensional Lorenz-63 model). It is demonstrated that the EGMF is capable of tracking systems with non-Gaussian uni- and multimodal ensemble distributions. Malden Wiley-Blackwell 2012 12 Quarterly journal of the Royal Meteorological Society 138 662 222 233 10.1002/qj.898 Institut für Mathematik