Analysis of Model Variance for Ensemble Based Turbulence Modeling

Jiang, Nan; Kaya, Songul; Layton, William
April 2015
Computational Methods in Applied Mathematics;Apr2015, Vol. 15 Issue 2, p173
Academic Journal
No abstract available.


Related Articles

  • Resilience of Hybrid Ensemble/3DVAR Analysis Schemes to Model Error and Ensemble Covariance Error. Etherton, Brian J.; Bishop, Craig H. // Monthly Weather Review;May2004, Vol. 132 Issue 5, p1065 

    Previous idealized numerical experiments have shown that a straightforward augmentation of an isotropic error correlation matrix with an ensemble-based error correlation matrix yields an improved data assimilation scheme under certain conditions. Those conditions are (a) the forecast model is...

  • Spatiotemporal Behavior of the TIGGE Medium-Range Ensemble Forecasts**. Kipling, Zak; Primo, Cristina; Charlton-Perez, Andrew // Monthly Weather Review;Aug2011, Vol. 139 Issue 8, p2561 

    Using the recently developed mean--variance of logarithms (MVL) diagram, together with The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) archive of medium-range ensemble forecasts from nine different centers, an analysis is presented...

  • A First-Principle Based Multi-Layer Model for Turbulent Channel Flow. Wu, Y.; Chen, X.; She, Z. S.; Hussain, F. // AIP Conference Proceedings;9/28/2011, Vol. 1376 Issue 1, p54 

    A quantitative model for turbulent channel flow is presented, based on the multi-layer hypothesis and the Lie-group analysis. The model describes the mean velocity profiles in the whole flow domain at a large range of Reynolds numbers (Re)-in a good agreement with present DNS results. This...

  • Ensembles of jittered association rule classifiers. Azevedo, Paulo J.; Jorge, Alípio Mário // Data Mining & Knowledge Discovery;Jul2010, Vol. 21 Issue 1, p91 

    The ensembling of classifiers tends to improve predictive accuracy. To obtain an ensemble with N classifiers, one typically needs to run N learning processes. In this paper we introduce and explore Model Jittering Ensembling, where one single model is perturbed in order to obtain variants that...

  • The effect of turbulence on the collision rates of small cloud drops. Koziol, A.S.; Leighton, H.G. // Journal of the Atmospheric Sciences;7/1/96, Vol. 53 Issue 13, p1910 

    Examines significance of the turbulence on collisions and coalescence of small cloud drops. Effects of turbulence on growth rate of small drops; Calculations of trajectories based on linear Stokes hydrodynamics; Scale analysis; Resistance problems; Modeling of the turbulent velocity field.

  • Weighting climate model ensembles for mean and variance estimates. Haughton, Ned; Abramowitz, Gab; Pitman, Andy; Phipps, Steven // Climate Dynamics;Dec2015, Vol. 45 Issue 11/12, p3169 

    Projections based on climate model ensembles commonly assume that each individual model simulation is of equal value. When combining simulations to estimate the mean and variance of quantities of interest, they are typically unweighted. Exceptions to this approach usually fall into two...

  • Numerical study of chaos based on a shell model. Yagi, M.; Itoh, S.-I.; Itoh, K.; Fukuyama, A. // Chaos;Jun99, Vol. 9 Issue 2, p393 

    Reports on a numerical study of chaos based on a shell model. Model and basic equations; Analysis of turbulence driven by thermal instability; Numerical accuracy and convergence.

  • A Comparison of Breeding and Ensemble Transform Kalman Filter Ensemble Forecast Schemes. Wang, Xuguang; Bishop, Craig H. // Journal of the Atmospheric Sciences;5/1/2003, Vol. 60 Issue 9, p1140 

    The ensemble transform Kalman filter (ETKF) ensemble forecast scheme is introduced and compared with both a simple and a masked breeding scheme. Instead of directly multiplying each forecast perturbation with a constant or regional rescaling factor as in the simple form of breeding and the...

  • Doppler radar measurements of turbulence in marine stratiform cloud during ASTEX. Frisch, A.S.; Lenschow, D.H. // Journal of the Atmospheric Sciences;8/15/95, Vol. 52 Issue 16, p2800 

    Measures the vertical air motion in clouds during the Atlantic Stratocumulus Transition Experiment by using a cloud-sensing Doppler radar with a vertically pointing antenna. Cloud droplet model; Cloud turbulence measurements; Analysis of variance and skewness.

  • Evaluation of a Procedure to Correct Spatial Averaging in Turbulence Statistics from a Doppler Lidar by Comparing Time Series with an Ultrasonic Anemometer. Brugger, Peter; Träumner, Katja; Jung, Christina // Journal of Atmospheric & Oceanic Technology;Oct2016, Vol. 33 Issue 10, p2135 

    Doppler lidars are frequently used for wind measurements in the atmospheric boundary layer, but their data are subject to spatial averaging due to the pulse length of the laser and sampling frequency of the return signal. This spatial averaging also affects estimates of turbulence statistics...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics