Aimé Fournier

A compatible and conservative spectral element method on unstructured grids

We derive a formulation of the spectral element method which is compatible on very general unstructured three-dimensional grids. Here compatible means that the method retains discrete analogs of several key properties of the divergence, gradient and curl operators: the divergence and gradient are anti-adjoints (the negative transpose) of each other, the curl is self-adjoint and annihilates the gradient operator, and the divergence annihilates the curl. The adjoint relations hold globally, and at the element level with the inclusion of a natural discrete element boundary flux term. We then discretize the shallow-water equations on the sphere using the cubed-sphere grid and show that compatibility allows us to locally conserve mass, energy and potential vorticity. Conservation is obtained without requiring the equations to be in conservation form. The conservation is exact assuming exact time integration.

Model Uncertainty in a Mesoscale Ensemble Prediction System: Stochastic versus Multiphysics Representations

AbstractA multiphysics and a stochastic kinetic-energy backscatter scheme are employed to represent model uncertainty in a mesoscale ensemble prediction system using the Weather Research and Forecasting model. Both model-error schemes lead to significant improvements over the control ensemble system that is simply a downscaled global ensemble forecast with the same physics for each ensemble member. The improvements are evident in verification against both observations and analyses, but different in some details. Overall the stochastic kinetic-energy backscatter scheme outperforms the multiphysics scheme, except near the surface. Best results are obtained when both schemes are used simultaneously, indicating that the model error can best be captured by a combination of multiple schemes.

The Spectral Element Atmosphere Model (SEAM): High-Resolution Parallel Computation and Localized Resolution of Regional Dynamics

Abstract Fast, accurate computation of geophysical fluid dynamics is often very challenging. This is due to the complexity of the PDEs themselves and their initial and boundary conditions. There are several practical advantages to using a relatively new numerical method, the spectral-element method (SEM), over standard methods. SEM combines spectral-method high accuracy with the geometric flexibility and computational efficiency of finite-element methods. This paper is intended to augment the few descriptions of SEM that aim at audiences besides numerical-methods specialists. Advantages of SEM with regard to flexibility, accuracy, and efficient parallel performance are explained, including sufficient details that readers may estimate the benefit of applying SEM to their own computations. The spectral element atmosphere model (SEAM) is an application of SEM to solving the spherical shallow-water or primitive equations. SEAM simulated decaying Jovian atmospheric shallow-water turbulence up to resolution T10...

High-Resolution Mesh Convergence Properties and Parallel Efficiency of a Spectral Element Atmospheric Dynamical Core

We first demonstrate the parallel performance of the dynamical core of a spectral element atmospheric model. The model uses continuous Galerkin spectral elements to discretize the surface of the Earth, coupled with finite differences in the radial direction. Results are presented from two distributed memory, mesh interconnect supercomputers (ASCI Red and BlueGene/L), using a two-dimensional space filling curve domain decomposition. Better than 80% parallel efficiency is obtained for fixed grids on up to 8938 processors. These runs represent the largest processor counts ever achieved for a geophysical application. They show that the upcoming Red Storm and BlueGene/L super-computers are well suited for performing global atmospheric simulations with a 10 km average grid spacing. We then demonstrate the accuracy of the method by performing a full three-dimensional mesh refinement convergence study, using the primitive equations to model breaking Rossby waves on the polar vortex. Due to the excellent parallel performance, the model is run at several resolutions up to 36 km with 200 levels using only modest computing resources. Isosurfaces of scaled potential vorticity exhibit complex dynamical features, e.g. a primary potential vorticity tongue, and a secondary instability causing roll-up into a ring of five smaller subvortices. As the resolution is increased, these features are shown to converge while potential vorticity gradients steepen.

Voronoi, Delaunay, and Block-Structured Mesh Refinement for Solution of the Shallow-Water Equations on the Sphere

Abstract Alternative meshes of the sphere and adaptive mesh refinement could be immensely beneficial for weather and climate forecasts, but it is not clear how mesh refinement should be achieved. A finite-volume model that solves the shallow-water equations on any mesh of the surface of the sphere is presented. The accuracy and cost effectiveness of four quasi-uniform meshes of the sphere are compared: a cubed sphere, reduced latitude–longitude, hexagonal–icosahedral, and triangular–icosahedral. On some standard shallow-water tests, the hexagonal–icosahedral mesh performs best and the reduced latitude–longitude mesh performs well only when the flow is aligned with the mesh. The inclusion of a refined mesh over a disc-shaped region is achieved using either gradual Delaunay, gradual Voronoi, or abrupt 2:1 block-structured refinement. These refined regions can actually degrade global accuracy, presumably because of changes in wave dispersion where the mesh is highly nonuniform. However, using gradual refinem...

Geophysical-astrophysical spectral-element adaptive refinement (GASpAR): Object-oriented h-adaptive fluid dynamics simulation

We present an object-oriented geophysical and astrophysical spectral-element adaptive refinement (GASpAR) code for application to turbulent flows. Like most spectralelement codes, GASpAR combines finite-element efficiency with spectral-method accuracy. It is also designed to be flexible enough for a range of geophysics and astrophysics applications where turbulence or other complex multiscale problems arise. For extensibility and flexibilty the code is designed in an object-oriented manner. The computational core is based on spectral-element operators, which are represented as objects. The formalism accommodates both conforming and non

Geophysical-astrophysical spectral-element adaptive refinement (GASpAR): Object-oriented h-adaptive code for geophysical fluid dynamics simulation

We present an object-oriented geophysical and astrophysical spectral-element adaptive refinement (GASpAR) code for application to turbulent flows. Like most spectralelement codes, GASpAR combines finite-element efficiency with spectral-method accuracy. It is also designed to be flexible enough for a range of geophysics and astrophysics applications where turbulence or other complex multiscale problems arise. For extensibility and flexibilty the code is designed in an object-oriented manner. The computational core is based on spectral-element operators, which are represented as objects. The formalism accommodates both conforming and non

Climate Modeling with Spectral Elements

As an effort toward improving climate model–component performance and accuracy, an atmosphericcomponent climate model has been developed, entitled the Spectral Element Atmospheric Climate Model and denoted as CAM_SEM. CAM_SEM includes a unique dynamical core coupled at this time to the physics component of the Community Atmosphere Model (CAM) as well as the Community Land Model. This model allows the inclusion of local mesh refinement to seamlessly study imbedded higher-resolution regional climate concurrently with the global climate. Additionally, the numerical structure of the model based on spectral elements allows for application of state-of-the-art computing hardware most effectively and economically to produce the best prediction/simulation results with minimal expenditure of computing resources. The model has been tested under various conditions beginning with the shallow water equations and ending with an Atmospheric Model Intercomparison Project (AMIP)-style run that uses initial conditions and physics comparable to the CAM2 (version 2 of the NCAR CAM climate model) experiments. For uniform resolution, the output of the model compares favorably with the published output from the CAM2 experiments. Further integrations with local mesh refinement included indicate that while greater detail in the prediction of mesh-refined regions—that is, regional climate—is observed, the remaining coarse-grid results are similar to results obtained from a uniform-grid integration of the model with identical conditions. It should be noted that in addition to spectral elements, other efficient schemes have lately been considered, in particular the finite-volume scheme. This scheme has not yet been incorporated into CAM_SEM. The two schemes—finite volume and spectral element—are quasi-independent and generally compatible, dealing with different aspects of the integration process. Their impact can be assessed separately and the omission of the finite-volume process herein will not detract from the evaluation of the results using the spectral-element method alone.

Introduction to Orthonormal Wavelet Analysis with Shift Invariance: Application to Observed Atmospheric Blocking Spatial Structure

Abstract Orthonormal wavelet analysis (OWA) is a special form of wavelet analysis, especially suitable for analyzing spatial structures, such as atmospheric fields. For this purpose, OWA is much more efficient and accurate than the nonorthogonal wavelet transform (WT), which was introduced to the meteorological community recently and which is more suitable for time series analysis. Whereas the continuous WT is strictly correct only for infinite domains, OWA is derived from periodizing and discretizing the infinite-domain case and so is correct for periodic boundary conditions. Unlike Fourier spectra, OWA is not shift invariant. Nor is it equivariant like the WT; that is, the OWA output does not shift as its input shifts. Two remedies are to combine all possible shifts, known as the overcomplete, nonorthogonal shift equivariant WT, or else to use a “best shift,” known as best shift wavelet analysis. Although shift invariant and orthonormal w.r.t. arbitrary inputs, the latter’s optimization generally depend...

Model‐uncertainty quantification in seismic tomography: method and applications

Uncertainty is inherent in every stage of the oil and gas exploration and production (E&P) business and understanding uncertainty enables mitigation of E&P risks. Therefore, quantification of uncertainty is beneficial for decision making and uncertainty should be managed along with other aspects of business. For example, decisions on well positioning should take into account the structural uncertainty related to the non-uniqueness of a velocity model used to create a seismic depth image. Moreover, recent advances in seismic acquisition technology, such as full-azimuth, long-offset techniques, combined with high-accuracy migration algorithms such as reverse-time migration, can greatly enhance images even in highly complex structural settings, provided that an Earth velocity model with sufficient resolution is available. Modern practices often use non-seismic observation to better constrain velocity model building. However, even with additional information, there is still ambiguity in our velocity models caused by the inherent non-uniqueness of the seismic experiment. Many different Earth velocity models exist that match the observed seismic (and well) data and this ambiguity grows rapidly away from well controls. The result is uncertainty in the seismic velocity model and the true positions of events in our images. Tracking these uncertainties can lead to significant improvement in the quantification of exploration risk (e.g., trap failure when well-logging data are not representative), drilling risk (e.g., dry wells and abnormal pore pressure) and volumetric uncertainties. Whilst the underlying ambiguity can never be fully eradicated, a quantified measure of these uncertainties provides a valuable tool for understanding and evaluating the risks and for development of better risk-mitigation plans and decision-making strategies