Nested Grid Research Articles

Simulation of the Mediterranean tsunami generated by the Mw 6.0 event offshore Bejaia (Algeria) on 18 March 2021

SUMMARY On 18 March 2021 an earthquake of magnitude Mw = 6.0 occurred offshore the Algerian coasts and generated a tsunami with offshore amplitudes smaller than a few millimetres crossing the western Mediterranean Sea. The objective of this study is threefold: first, to determine whether seismic sources calculated in the context of tsunami early warning are relevant; secondly, to determine whether tsunami simulations are able to reproduce tide-gauge observations and thirdly, to define the sensitivity of simulations to the grid resolutions and tsunami parameters. In the Mediterranean Sea, a very small number of available coastal tide gauges recorded the tsunami. Among them, a few French tide gauge stations recorded water waves with amplitudes smaller than a few centimetres and with periods ranging from 5 to 20 min associated to harbour or bay resonances. Numerical simulations of the tsunami are performed by the operational code Taitoko for seven different source fault models. Three of them allow for a rapid source detection and characterization in the framework of tsunami warning at CENALT (Centre National d'Alerte aux Tsunamis, France). The integrated code Taitoko uses a system of multiple nested grids. Standard Boussinesq equations are solved in the Mediterranean grid, whereas non-linear shallow water equations are solved in coastal and harbour grids with 25 and 5 m resolutions, respectively. Whatever the fault model, the observed time-series of water heights are reproduced satisfactorily both in phase and amplitude by the model at Nice and Monaco but poorly at Port Mahon (Minorca) and Toulon.

Improvement to stochastic tsunami hazard analysis of megathrust earthquakes for western Makran subduction zone

The Makran Subduction Zone (MSZ) is one of the world's most tsunami-prone regions. In the present study, tsunami hazards associated with potential MSZ earthquakes are evaluated using stochastic earthquake rupture modeling, which allows for the incorporation of uncertainties related to slip heterogeneity. Along the western segment of the MSZ, where tsunami hazard studies have received less attention, megathrust earthquakes with moment magnitudes of Mw 8.5, 8.7, and 8.9 are considered. The source parameters of 600 stochastic earthquake scenarios are calculated using the scaling relationships of Goda et al. (2016), which represent significant improvements over previous scaling relationships by taking into account the uncertainty of multiple source parameters and the coverage of a wide range of earthquake magnitudes. In order to simulate tsunami propagation and inundation, the nonlinear shallow water equations are used in four-level nested grid model domains .According to the calculated slip fields, the seismic source characteristics provided by stochastic source models significantly change within the earthquakes scenarios with identical magnitudes and may completely differ from the results of traditional uniform-slip source models. The results of the 600 Monte Carlo tsunami simulations demonstrate that even extremely large earthquakes in the west MSZ do not lead to significant tsunamis along the eastern coastal regions of Makran. The average maximum wave heights of 8, 10, and 17 m along the western Makran coasts, which correspond to the Mw 8.5, 8.7, and 8.9 scenarios, respectively, emphasize the severe potential threat of a possible tsunamigenic event in the west of the MSZ to the Iranian coasts. In addition, the significant difference between the maximum wave heights of the 10th and 90th percentiles for each scenario demonstrates the large variability of tsunami heights resulting from uncertainties associated with stochastic seismic source modeling. Thus, it can be concluded that the tsunami hazard parameters estimated by simple uniform-slip source models need to be revised in future tsunami risk assessment studies.

An evaluation method for wake effect of wind farm group based on CFD-WRF coupled wind resource map

The wake effect of wind farm can reduce the incoming wind speed at the wind turbine located in the downstream direction, resulting in the decrease of global output. WRF model adopts a three-layer two-way nested grid division scheme to simulate the upper atmospheric circulation, obtain wind speed, wind direction and other data that can truly reproduce the fluid characteristics of the regional wind farm group. The boundary conditions and solution conditions of CFD model are set, and the computational fluid dynamics model of the region is obtained. WRF is coupled with CFD, and Fitch wake model is introduced into it. By introducing the drag coefficient of wind turbine into the calculation of wind speed and turbulent kinetic energy in CFD-WRF coupling model, the wind field characteristics and wake effect of wind farm are simulated online. Monte Carlo sampling method is used to obtain random wind resource data in CFD-WRF coupling model, and then the sampled data is used to calculate the group output of wind farms, and evaluate the impact of wake effect on wind farm treatment. The experimental results show that this method can effectively analyze the characteristic data of regional wind field, and the calculation time of RANS method is about 3 s. Due to the wake effect, the overall output and efficiency of wind field will be significantly reduced.

Optimized Software Tools to Generate Large Spatio-Temporal Data Using the Datacubes Concept: Application to Crop Classification in Cap Bon, Tunisia

In the context of a changing climate, monitoring agricultural systems is becoming increasingly important. Remote sensing products provide essential information for the crop classification application, which is used to produce thematic maps. High-resolution and regional-scale maps of agricultural land are required to develop better adapted future strategies. Nevertheless, the performance of crop classification using large spatio-temporal data remains challenging due to the difficulties in handling huge amounts of input data (different spatial and temporal resolutions). This paper proposes an innovative approach of remote sensing data management that was used to prepare the input data for the crop classification application. This classification was carried out in the Cap Bon region, Tunisia, to classify citrus groves among two other crop classes (olive groves and open field) using multi-temporal remote sensing data from Sentinel- 1 and Sentinel-2 satellite platforms. Thus, we described the new QGIS plugin “Model Management Tool (MMT)”. This plugin was designed to manage large Earth observation (EO) data. This tool is based on the combination of two concepts: (i) the local nested grid (LNG) called Tuplekeys and (ii) Datacubes. Tuplekeys or special spatial regions were created within a LNG to allow a proper integration between the data of both sensors. The Datacubes concept allows to provide an arranged array of time-series multi-dimensional stacks (space, time and data) of gridded data. Two different classification processes were performed based on the selection of the input feature (the obtained time-series as input data: NDVI and NDVI + VV + VH) and on the most accurate algorithm for each scenario (22 tested classifiers). The obtained results revealed that the best classification performance and highest accuracy were obtained with the scenario using only optical-based information (NDVI), with an overall accuracy OA = 0.76. This result was obtained by support vector machine (SVM). As for the scenario relying on the combination of optical and SAR data (NDVI + VV + VH), it presented an OA = 0.58. Our results demonstrate the usefulness of the new data management tool in organizing the input classification data. Additionally, our results highlight the importance of optical data to provide acceptable classification performance especially for a complex landscape such as that of the Cap Bon. The information obtained from this work will allow the estimation of the water requirements of citrus orchards and the improvement of irrigation scheduling methodologies. Likewise, many future methodologies will certainly rely on the combination of Tuplekeys and Datacubes concepts which have been tested within the MMT tool.

Classification and feature analysis of the Human Connectome Project dataset for differentiating between males and females

We analysed features relevant for differentiation between males and females based on the data available from the Human Connectome Project (HCP) S1200 dataset. We used 354 features containing cognitive and emotional measures as well as measures derived from task functional magnetic resonance imaging (MRI) and structural brain MRI. The paper presents a thorough analysis of this extensive set of features using a machine learning approach with a goal of identifying features that have the ability to differentiate between males and females. We used two state of the art classification algorithms with different properties: support vector machine (SVM) and random forest classifier (RFC). For each classifier the hyperparameters were obtained and classifiers were optimized using nested cross validation and grid search. This resulted in the classification performance of 91% and 89% accuracy using SVM and RFC, respectively. Using SHAP (SHapley Additive exPlanations) method we obtained relevance of features as indicators of sex differences and identified features with high discriminative power for sex classification. The majority of top features were brain morphological measures, and only a small proportion were features related to cognitive performance. Our results demonstrate the importance and advantages of using a machine learning approach when analysing sex differences.

Modelling of surface river plume using set-up and input data files of Delft-3D model.

This data article presents set-up and input data files to model a surface river plume through curvilinear nested grid in double-way mode. The hydrodynamic modelling in river deltas with intense transport processes and complex bathymetry such as the Magdalena River delta, requires a mesh grid that ease the natural river discharge into the ocean. The aforementioned may be challenging due to the numerical scheme and stability restrictions of the numerical models that difficult having efficient and effective validated simulations. This dataset files are a reference to perform analysis of the hydrodynamic river deltas, meaningful for optimizing time and resources, easing the planning of measurement campaigns what reduce risks of the personnel and instrumentation during equipment deployment and field work .The application of the set-up and input data files of this data article is shown in Rueda-Bayona et al. [1].

Wavelet Methods and Adaptive Grids in One-Dimensional Movable Boundary Problems

Adaptive wavelet collocation methods use wavelet transform and filtering to generate adaptive grids. However, if the boundary moves, the grid becomes aberrant. It baffles wavelet transform and makes the adaptive wavelet methods lose advantages on computational efficiency. This paper develops a series of methods or skills to effectively perform wavelet transform and to generate adaptive grids for one-dimensional movable boundary problems. The methods remain the original inner grid points and keep the grid in the original-nested structure, in order to remain scanty during the whole computing process. For boundary extending, the adaptive wavelet program begins to run on the very new grid beyond the original boundary once it reaches a nested structure, which is called the Intermittent Adaptive Method as a consequence. If the boundary extends tremendously, the new nested grids can be combined to a greater nested grid for further efficiency, which is named the Grid Combine Method. While for boundary contracting, a fictitious boundary is addressed to replace the original boundary that will recede, so wavelet transform can be successfully performed on the original nested grid. Finally, two numerical tests, local features moving and gas gun, were resolved and discussed to show the evolution process of the adaptive grids with the boundaries moving. For boundary contracting, the valid points decrease because the computation domain recedes; while for boundary extending, the valid point numbers vary between a range that almost remains unchanged.

Multi‐Grid Nesting Ability to Represent Convections Across the Gray Zone

AbstractThis study investigated the multi‐grid nesting ability of a limited area model to effectively represent convections across the gray zone, the resolution around 1–10 km where both cumulus parameterization and explicit convection are problematic. It evaluated the sensitivity of Meiyu rainfall forecasts in Jiangsu, China to model configurations of grid nesting and convection treatment. These configurations consisted of grid spacings from 30, 15, 9, 5, 3 to 1 km, single or double or triple nested grids, and the traditional Kain‐Fritsch (KF) or scale‐aware Grell‐Freitas cumulus parameterization or the explicit convection in the outer domain [O]. In single nesting [O], coarse grids (>3–5 km) required parameterization to represent organized cumuli, while explicitly resolving convections in finer grids were necessary to improve forecasts. In double nesting [O] using cumulus parameterization at 30–9 km with the inner domain [I] using explicit convection at 1 km, the nesting ratio could be as large as 30 without significantly impacting [I] forecasts. This suggests a pragmatic approach to avoid the challenge in representing convections across the gray zone. Using Grell‐Freitas may improve mean [O] rainfall distributions, but this was not true for [I] forecasts due to counter errors in space and time, which were larger than using KF and at coarser grids. Triple nesting with a middle 3‐ or 5‐km grid was unnecessary and could even degrade [I] forecasts. Nesting [O] using KF to parameterize cumuli at 15 km with [I] explicitly resolving convections at 1 km achieved the best overall rainfall forecast in Jiangsu.

Numerical Prediction of Background Buildup of Salinity Due to Desalination Brine Discharges into the Northern Arabian Gulf

Brine discharges from desalination plants into low-flushing water bodies are challenging from the point of view of dilution, because of the possibility of background buildup effects that decrease the overall achievable dilution. To illustrate the background buildup effect, this paper uses the Arabian (Persian) Gulf, a shallow, reverse tidal estuary with only one outlet available for exchange flow. While desalination does not significantly affect the long-term average Gulf-wide salinity, due to the mitigating effect of the Indian Ocean Surface Water inflow, its resulting elevated salinities, as well as elevated concentrations of possible contaminants (such as heavy metals and organophosphates), can affect marine environments on a local and regional scale. To analyze the potential effect of background salinity buildup on dilutions achievable from discharge locations in the northern Gulf, a 3-dimensional hydrodynamic model (Delft3D) was used to simulate brine discharges from a single hypothetical source location along the Kuwaiti shoreline, about 900 km from the Strait of Hormuz. Using nested grids with a horizontal resolution, comparable to a local tidal excursion (250 m), far field dilutions of about 28 were computed for this discharge location. With this far field dilution, to achieve a total dilution of 20, the near field dilution (achievable using a submerged diffuser) would need to be increased to approximately 70. Conversely, the background build-up means that a near field dilution of 20 yields a total dilution of only about 12.

Sparse grid discontinuous Galerkin methods for the Vlasov-Maxwell system

In this paper, we develop sparse grid discontinuous Galerkin (DG) schemes for the Vlasov-Maxwell (VM) equations. The VM system is a fundamental kinetic model in plasma physics, and its numerical computations are quite demanding, due to its intrinsic high-dimensionality and the need to retain many properties of the physical solutions. To break the curse of dimensionality, we consider the sparse grid DG methods that were recently developed in [20,21] for transport equations. Such methods are based on multiwavelets on tensorized nested grids and can significantly reduce the numbers of degrees of freedom. We formulate two versions of the schemes: sparse grid DG and adaptive sparse grid DG methods for the VM system. Their key properties and implementation details are discussed. Accuracy and robustness are demonstrated by numerical tests, with emphasis on comparison of the performance of the two methods, as well as with their full grid counterparts.

Scalable pixel-based crop classification combining Sentinel-2 and Landsat-8 data time series: Case study of the Duero river basin

AbstractSatellite imagery is the foremost source of information to analyze and monitor land covers in several time ranges, especially over large areas. However, it is not always either freely available or easily compatible for the final users due to the different resolutions offered by sensors onboard the satellite platforms. Crop classification is an important task to control and make decisions related to the agricultural practice and its regulation. However, it is not trivial, especially for extensive areas. Thus, this paper proposes a new approach for crop classification in large areas by a combined use of multi-temporal open-source remote sensing data from Sentinel-2 (S2) and Landsat-8 (L8) satellite platforms. Having to deal with different spatial and temporal resolutions, special spatial regions (called Tuplekeys) were created within a local nested grid to allow a proper integration between the data of both sensors. Temporal variation of the Normalized Difference Vegetation Index (NDVI) was the chosen input to classify crops. Moreover, due to the massive quantity of data collected, filters considering some agronomic and edaphic criteria were applied with the dual goal of decreasing redundancies and increasing the process efficiency. Out of three different machine learning classifiers analyzed, a plot-based approach was considered for the algorithms calibration while a pixel-based approach was used for the final classification process. The methodology was both tested and validated in the Duero river basin (Spain), 78,859 km2, for the 2017 spring and summer seasons. Finally, classification outputs were analyzed throughout their overall accuracy (OA), not only for the whole basin but also for each of the Tuplekeys so that the OA spatial distribution was evaluated as well. The Ensemble Bagged Trees (EBT) algorithm showed the maximum OA, 87% and 92%, when classifying crops individually (15 classes) and grouped (7 classes), respectively, proving both the accuracy and efficiency of the developed approach.

Wave Resource Characterization Using an Unstructured Grid Modeling Approach

This paper presents a modeling study conducted on the central Oregon coast for wave resource characterization, using the unstructured grid Simulating WAve Nearshore (SWAN) model coupled with a nested grid WAVEWATCH III® (WWIII) model. The flexibility of models with various spatial resolutions and the effects of open boundary conditions simulated by a nested grid WWIII model with different physics packages were evaluated. The model results demonstrate the advantage of the unstructured grid-modeling approach for flexible model resolution and good model skills in simulating the six wave resource parameters recommended by the International Electrotechnical Commission in comparison to the observed data in Year 2009 at National Data Buoy Center Buoy 46050. Notably, spectral analysis indicates that the ST4 physics package improves upon the ST2 physics package’s ability to predict wave power density for large waves, which is important for wave resource assessment, load calculation of devices, and risk management. In addition, bivariate distributions show that the simulated sea state of maximum occurrence with the ST4 physics package matched the observed data better than with the ST2 physics package. This study demonstrated that the unstructured grid wave modeling approach, driven by regional nested grid WWIII outputs along with the ST4 physics package, can efficiently provide accurate wave hindcasts to support wave resource characterization. Our study also suggests that wind effects need to be considered if the dimension of the model domain is greater than approximately 100 km, or O (102 km).

Algebraic dynamic multilevel method for compositional flow in heterogeneous porous media

This paper presents the algebraic dynamic multilevel method (ADM) for compositional flow in three dimensional heterogeneous porous media in presence of capillary and gravitational effects. As a significant advancement compared to the ADM for immiscible flows (Cusini et al., 2016) [33], here, mass conservation equations are solved along with k-value based thermodynamic equilibrium equations using a fully-implicit (FIM) coupling strategy. Two different fine-scale compositional formulations are considered: (1) the natural variables and (2) the overall-compositions formulation. At each Newton's iteration the fine-scale FIM Jacobian system is mapped to a dynamically defined (in space and time) multilevel nested grid. The appropriate grid resolution is chosen based on the contrast of user-defined fluid properties and on the presence of specific features (e.g., well source terms). Consistent mapping between different resolutions is performed by the means of sequences of restriction and prolongation operators. While finite-volume restriction operators are employed to ensure mass conservation at all resolutions, various prolongation operators are considered. In particular, different interpolation strategies can be used for the different primary variables, and multiscale basis functions are chosen as pressure interpolators so that fine scale heterogeneities are accurately accounted for across different resolutions. Several numerical experiments are conducted to analyse the accuracy, efficiency and robustness of the method for both 2D and 3D domains. Results show that ADM provides accurate solutions by employing only a fraction of the number of grid-cells employed in fine-scale simulations. As such, it presents a promising approach for large-scale simulations of multiphase flow in heterogeneous reservoirs with complex non-linear fluid physics.

The 1979 Submarine Landslide-Generated Tsunami in Mururoa, French Polynesia

This paper aims at best describing the submarine landslide which induced partial submersion of the atolls of Mururoa and Fangataufa in 1979. More precisely, waves propagated along the south coast of Mururoa atoll and penetrated into its lagoon some minutes after the landslide triggering (t = 0 s), whereas a train of eight water waves reached the runway located on the north-east coast of Fangataufa (40 km south of Mururoa) between t = 7 min 30 s and t = 20 min. A numerical model based on shallow water equations is used to simulate the landslide as well as the associated tsunami. Saint-Venant equations are used to propagate the tsunami in coastal areas, whereas the offshore propagation is simulated by solving weakly nonlinear Boussinesq equations. Low- and high-resolution nested grids are used to simulate the tsunami propagation in deep sea and in shallow waters, respectively. Several scenarios have been tested to reproduce the observed water and run-up heights in the near and far fields. The best scenarios correspond to a landslide with a volume in the range (75–90 Mm3) (for a basal friction angle of 35°) and with a basal friction angle in the range (30°–40°) (for a volume of 80 Mm3). These results have been completed by a parametric study on the slide parameters.

An Adaptive Multiresolution Discontinuous Galerkin Method for Time-Dependent Transport Equations in Multidimensions

In this paper, we develop an adaptive multiresolution discontinuous Galerkin (DG) scheme for time-dependent transport equations in multidimensions. The method is constructed using multiwavlelets on tensorized nested grids. Adaptivity is realized by error thresholding based on the hierarchical surplus, and the Runge--Kutta DG scheme is employed as the reference time evolution algorithm. We show that the scheme performs similarly to a sparse grid DG method when the solution is smooth, reducing computational cost in multidimensions. When the solution is no longer smooth, the adaptive algorithm can automatically capture fine local structures. The method is therefore very suitable for deterministic kinetic simulations. Numerical results including several benchmark tests, the Vlasov--Poisson (VP), and oscillatory VP systems are provided.

Controlled accuracy finite element estimates for the effective stiffness of composites with spherical inclusions

Mixed form finite element method was used to study the microstructural effects in the effective stiffness of composites reinforced or weakened by regular and random dispersions of non-overlapping identical spheres. Controlled accuracy stiffness estimates were achieved by using the unstructured mesh polynomial extension and asymptotic nested grid extension of the finite element method. Periodic regular and random microstructure computer models with up to a hundred spheres were considered. The significance of the elastostatic bridges separating near-to-touch spheres was studied and their impact on the effective stiffness was assessed. It was found that for a variety of practically relevant composites with moderate stiffness contrast between the phases, the effective stiffness estimates were remarkably insensitive to the underlying composite microstructure. The obtained finite element estimates were compared with predictions of the generalized self-consistent (GSC) model ( Christensen and Lo, 1979 ) and it was shown that the model was accurate over the entire range of practically relevant sphere fractions. We studied a high stiffness contrast composite consisting of a near incompressible rubber matrix filled with rigid spherical inclusions. In this case strong microstructural effects were observed but, nevertheless, for the random composite obeying the hard sphere Percus–Yevick statistics the GSC model remained strikingly accurate over the whole range of practically relevant inclusion fractions. The asymptotic Einstein limit GSC solution was derived and it was demonstrated that it gave accurate micromechanics-based solution of the classical Einstein homogenization problem of a random dispersion of perfectly rigid spherical inclusions in a fully incompressible matrix.

Refined numerical simulation in wind resource assessment

A coupled model system for Wind Resource Assessment (WRA) was studied. Using a mesoscale meteorological model, the Weather Research and Forecasting (WRF) model, global-scale data were downscaled to the inner nested grid scale (typically a few kilometers), and then through the coupling Computational Fluid Dynamics (CFD) mode, FLUENT. High-resolution results (50 m in the horizontal direction; 10 m in the vertical direction below 150 m) of the wind speed distribution data and ultimately refined wind farm information, were obtained. The refined WRF/FLUENT system was then applied to assess the wind resource over complex terrain in the northern Poyang Lake region. The results showed that the approach is viable for the assessment of wind energy.

Petroleum Reservoir Simulation Using Super Element Method

This paper describes the theoretical framework and results of using a rapid three-dimensional super element model of oil field development. The model makes it possible to speed up the calculation of two-phase filtration in an oil reservoir by hundreds of times due to the use of large computational cells – super elements, the number of which corresponds to the number of wells in the field. Accuracy of the numerical solution is ensured by a formulation of the problem in terms of the smooth mean fields of pressure and saturation with a selective refinement of the solution in the vicinity of the wells on independent detailed nested grids.

Global-to-Regional Nested Grid Climate Simulations in the GFDL High Resolution Atmospheric Model

A two-way nested grid version of the Geophysical Fluid Dynamics Laboratory High Resolution Atmosphere Model (HiRAM) has been developed that uses simple methods for providing nested grid boundary conditions and mass-conserving nested-to-global communication. Nested grid simulations over the Maritime Continent and over North America were performed, each at two different resolutions: a 110-km mean grid cell width refined by a factor of 3, and a 50-km mean grid cell width refined by a factor of 2. Nested grid simulations were compared against uniform-resolution simulations, and against reanalyses, to determine the effect of grid nesting on both the modeled global climate and the simulation of small-scale features. Orographically forced precipitation was robustly found to be simulated with more detail and greater realism in a nested grid simulation compared with when only the coarse grids were simulated alone. Tropical precipitation biases were reduced in the Maritime Continent region when a nested grid was introduced. Both results were robust to changes in the nested grid parameterization tunings. In North America, cold-season orographic precipitation was improved by nesting, but precipitation biases in the central and eastern United States were little changed. Improving the resolution through nesting also allowed for more intense rainfall events, greater Kelvin wave activity, and stronger tropical cyclones. Nested grid boundary artifacts were more pronounced when a one-way, noninteractive nested grid was used.

Numerical Prediction of the 8 May 2003 Oklahoma City Tornadic Supercell and Embedded Tornado Using ARPS with the Assimilation of WSR-88D Data

Abstract The 8 May 2003 Oklahoma City, Oklahoma, tornadic supercell is predicted with the Advanced Regional Prediction System (ARPS) model using four nested grids with 9-km, 1-km, 100-m, and 50-m grid spacings. The Oklahoma City Weather Surveillance Radar-1988 Doppler (WSR-88D) radial velocity and reflectivity data are assimilated through the ARPS three-dimensional variational data assimilation (3DVAR) and cloud analysis on the 1-km grid to generate a set of initial conditions that includes a well-analyzed supercell and associated low-level mesocyclone. Additional 1-km experiments show that the use of radial velocity and the proper use of a divergence constraint in the 3DVAR play an important role in the establishment of the low-level mesocyclone during the assimilation and forecast. Assimilating reflectivity data alone failed to predict the mesocyclone intensification. The 100-m grid starts from the interpolated 1-km control initial conditions, while the further nested 50-m grid starts from the 20-min forecast on the 100-m grid. The forecasts on both grids cover the entire period of the observed tornado outbreak and successfully capture the development of tornadic vortices. The intensity of a tornado on the 50-m grid reaches the high end of category 3 on the Fujita scale (F3), while the corresponding simulated tornado on the 100-m grid reaches F2 intensity. The timing of the tornadogenesis on both grids agrees with the observations very well, although the predicted tornado was slightly weaker and somewhat shorter lived. The predicted tornado track parallels the observed damage track although it is displaced northward by about 8 km. The predicted tornado vortices have realistic structures similar to those documented in previous theoretical, idealized modeling and some observational studies. The prediction of an observed tornado in a supercell with a similar degree of realism has not been achieved before.