Global ETD Search

Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.

Results

Showing 1 to 4 of 4 for “"Ensemble-based methods"”.

  1. Efficient formulation and implementation of ensemble based methods in data assimilation

    Ensemble-based methods have gained widespread popularity in the field of data assimilation. An ensemble of model realizations encapsulates information about the error correlations driven by the physics and the dynamics of the numerical model. This information can be used to obtain improved …

    vt Repository record for Efficient formulation and implementation of ensemble based methods in data assimilation (opens in a new tab)

  2. Probabilistic state estimation in regimes of nonlinear error growth

    … prediction (NWP). Nearly all implementable methods of state estimation suitable for NWP are forced to assume that errors remain in regimes of linear error growth and retain distributions of Gaussian uncertainty, yet nonlinear systems like the atmosphere can readily allow regimes of nonlinear …

    mit Repository record for Probabilistic state estimation in regimes of nonlinear error growth (opens in a new tab)

  3. Self-accompaniment and improvisation in solo jazz piano: Practice-led investigations of assimilation, ostinatos and ‘hand splitting’

    … performing solo requires a different approach to ensemble work, jazz pianists are commonly only trained in ensemble practices, rendering the solo setting a potentially overwhelming challenge. In order to move away from ensemble-based methods, the project sought to develop pianistic techniques and …

    edithcowan Repository record for Self-accompaniment and improvisation in solo jazz piano: Practice-led investigations of assimilation, ostinatos and ‘hand splitting’ (opens in a new tab)

  4. Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification

    … data assimilation framework, specifically for ensemble based methods where the effect of sampling errors is alleviated by localization. Finally, we study the uncertainty in numerical weather prediction models coming from approximate descriptions of physical processes.

    vt Repository record for Probabilistic and Statistical Learning Models for Error Modeling and Uncertainty Quantification (opens in a new tab)