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A robust WENO scheme for nonlinear waves in a moving reference frame

Journal of Hydrodynamics 2016年第3期28卷 482-488页

作者： Stavros KONTOS Harry B.BINGHAM ole lindberg Allan P.ENGSIG-KARUP Department of Mechanical Engineering Technical University of DenmarkDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkDenmark

For robust nonlinear wave simulation in a moving reference frame, we recast the free surface problem in Hamilton-Jacobi form and propose a Weighted Essentially Non-Oscillatory （WENO） scheme to automatically handle the upwinding of the convective term. A new automatic procedure for deriving the linear WENO weights based on a Taylor series expansion is introduced. A simplified smoothness indicator is proposed and is shown to perform well. The scheme is combined with high-order explicit Runge-Kutta time integration and a dissipative Lax-Friedrichs-type flux to solve for nonlinear wave propagation in a moving frame of reference. The WENO scheme is robust and less dissipative than the equivalent order upwind-biased finite difference scheme for all ratios of frame of reference to wave propagation speed tested. This provides the basis for solving general nonlinear wave-structure interaction problems at forward speed.

关键词： nonlinear waves Weighted Essentially Non-Oscillatory (WENO) finite difference

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Why ecosystem characteristics predicted from remotely sensed data are unbiased and biased at the same time-and how this affects applications

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Forest Ecosystems 2024年第1期11卷 24-31页

作者： Goran Ståhl Terje Gobakken Svetlana Saarela Henrik J.Persson Magnus Ekstrom Sean P.Healey Zhiqiang Yang Johan Holmgren Eva lindberg Kenneth Nystrom Emanuele Papucci Patrik Ulvdal Hans oleØrka Erik Næsset Zhengyang Hou Håkan Olsson Ronald E.McRoberts Department of Forest Resource Management Swedish University of Agricultural Sciences90183UmeåSweden Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life SciencesÅsNorway USDA Forest Service Rocky Mountain Research StationOgdenUTUSA The Key Laboratory for Silviculture and Conservation of Ministry of Education Beijing Forestry UniversityBeijing100083China Department of Forest Resources University of MinnesotaSt.PaulMNUSA

Remotely sensed data are frequently used for predicting and mapping ecosystem characteristics,and spatially explicit wall-to-wall information is sometimes proposed as the best possible source of information for ***,wall-to-wall information typically relies on model-based prediction,and several features of model-based prediction should be understood before extensively relying on this type of *** such feature is that model-based predictors can be considered both unbiased and biased at the same time,which has important implications in several areas of *** this discussion paper,we first describe the conventional model-unbiasedness paradigm that underpins most prediction techniques using remotely sensed(or other)auxiliary *** this point of view,model-based predictors are typically ***,we show that for specific domains,identified based on their true values,the same model-based predictors can be considered biased,and sometimes severely *** suggest distinguishing between conventional model-bias,defined in the statistical literature as the difference between the expected value of a predictor and the expected value of the quantity being predicted,and design-bias of model-based estimators,defined as the difference between the expected value of a model-based estimator and the true value of the quantity being *** show that model-based estimators(or predictors)are typically design-biased,and that there is a trend in the design-bias from overestimating small true values to underestimating large true ***,we give examples of applications where this is important to acknowledge and to potentially make adjustments to correct for the design-bias *** argue that relying entirely on conventional model-unbiasedness may lead to mistakes in several areas of application that use predictions from remotely sensed data.

关键词： Bias Model-based inference Design-based inference

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