316 research outputs found
Flexible Simulation of Deformable Models Using Discontinuous Galerkin FEM
No full textWe propose a simulation technique for elastically deformable objects based on the discontinuous Galerkin finite element method (DG FEM). In contrast to traditional FEM, it overcomes the restrictions of conforming basis functions by allowing for discontinuous elements with weakly enforced continuity constraints. This added flexibility enables the simulation of arbitrarily shaped, convex and non-convex polyhedral elements, while still using simple polynomial basis functions. For the accurate strain integration over these elements we propose an analytic technique based on the divergence theorem. Being able to handle arbitrary elements eventually allows us to derive simple and efficient techniques for volumetric mesh generation, adaptive mesh refinement, and robust cuttingEurographics/SIGGRAPH Symposium on Computer Animatio
High Resolution (sub 1 km) weather forecasting enhanced with ensembles
No full textLos pronósticos acertados salvan vidas, ya sea mediante una alerta oportuna durante una emergencia o en la mitigación de eventos, y permitiendo prevenir pérdidas económicas debidas a fenómenos meteorológicos. Debido a esto se han desarrollado formas de mejorar los pronósticos como: aumentar la resolución espacial del modelo o el uso de ensambles. Al aumentar la resolución del modelo los patrones y características de variables como la precipitación son más realistas, a pesar de esto, a menudo estos pronósticos ocurren en otro lugar o en un tiempo equivocado. Al hacer pronósticos mediante ensambles obtenemos información de la incertidumbre de los posibles futuros estados de la atmósfera, sin embargo, es necesario tener una mayor capacidad de cómputo. Es relativamente sencillo pronosticar la precipitación como un promedio a lo largo de grandes áreas, pero no lo es para una región específica. Por lo tanto, el objetivo de este trabajo es crear un pronóstico mediante ensambles de un modelo de mesoescala para un lugar específico, se espera que la información extra obtenida mediante los ensambles aporte mayor confianza al pronóstico determinístico o que nos proporcione otros posibles estados meteorológicos que no se muestren en este pronóstico. Para realizar el trabajo se utilizó el modelo Weather Research and Forecasting (WRF) con el cual se hizo una reducción de escala dinámica; ya que el interés es pronosticar lugares específicos la resolución horizontal del ensamble es menor a 1 km. Los ensambles fueron forzados con salidas del North American Mesoscale Forecast System (NAM) que cuentan con una resolución horizontal aproximada de 5 km; la zona de estudio está centrada en la ciudad de Ensenada dentro de una región de 180 x 180 km. Se evaluaron y analizaron los resultados del ensamble para encontrar la sensibilidad de éste a las perturbaciones en las salidas del modelo NAM (datos de entrada), número de miembros del ensamble y resolución de la malla. El sistema de ensambles genera su propia solución en la resolución más pequeña, lo cual es distinto a una simple interpolación. Los resultados se analizaron utilizando diferentes skills estadísticos, encontrando una mejora significativa en cada uno de estos.The accurate forecasting of the weather saves lives, either by producing timely early warnings of an upcoming emergency or essential updates during an extreme event or its future developments. Accurate forecasts allow the prevention of loss of lives and infrastructure due to extreme events. For this reason, there is a continuous drive to improve forecasts, for example by increasing the spatial resolution, data assimilation, and ensemble forecasts. By increasing the resolution, the meteorological phenomena usually appear more realistic. However, timing, location, and development may still be wrong. Forecasts using ensemble methods add vital information about the uncertainties of the predictions. However, to realize these forecasts more computational effort is required. For a forecast of average precipitation over large areas (50-200km) the model behavior is often reasonable, but when the information at a specific location is required - such as for emergency response - the performance drops significantly. Therefore the objective of this work is to create a system that provides a forecast using ensembles, starting from a mesoscale deterministic model. The forecast is to be specific for a particular location, to meet emergency response needs. It is expected that the ensemble will provide accurate results and also a measure of the forecasts probability. The deterministic model cannot provide these probabilities. The Weather Research and Forecasting (WRF) model will be used to create the ensemble and downscale the members of the ensemble to a sub 1km resolution. The ensemble will be driven by analyses and forecasts from the North American Mesoscale Forecast System (NAM) with 5km resolution. The study area is centered on the city of Ensenada, within an area of 180 x 180 km. The members of the ensemble will be analyzed to obtain an improved 60hr forecast in comparison to the original mesoscale forecast. It will be shown that the added resolution does generate a unique solution beyond simple interpolation. The results will be analyzed, and it will be demonstrated that the forecast skill improved significantly
Influence of Arctic sea ice loss on the winter variability of the jet stream
No full textEn este trabajo se estudió el efecto de la reducción de la extensión de hielo marino del Océano Ártico sobre la circulación atmosférica de latitudes medias del Hemisferio Norte. Se realizaron diferentes simulaciones numéricas con un modelo de circulación general (GCM, por sus siglas en inglés) en el que la extensión de hielo marino Ártico se redujo para provocar a largo plazo un incremento en la temperatura superficial de las latitudes altas similar al que se observa en el registro instrumental. Se realizaron dos experimentos de simulaciones globales con reducción de la extensión del hielo marino del Ártico: 1) simulaciones que, al sur de 70N, utilizan un ciclo anual climatológico de la temperatura de la superficie del mar (SST) y 2) el segundo utiliza la variabilidad total de la SST. El análisis se realizó para la estación invernal y los resultados se dividieron en dos secciones. En la primera parte se propusieron dos métricas para identificar las señales de calentamiento del Ártico enmascaradas por la variabilidad climática interna (VCI). Se usaron métricas basadas en el Earth Mover’s Distance (EMD) para evaluar las diferencias entre los experimentos numéricos, y la estructura espacial de la respuesta atmosférica al calentamiento del Ártico se analizó con el Índice de Similitud Estructural (SSIM, por sus siglas en inglés). Se encontró que el método estadístico estándar utilizado en estudios anteriores para probar la significancia estadística (la prueba t de Student) es propenso a resultados falsos positivos que podrían conducir a la atribución errónea de señales en la respuesta atmosférica al calentamiento del Ártico. Las dos métricas mostraron que pueden identificar diferencias persistentes en el nivel de 500hPa en la altura geopotencial de latitudes altas y de los campos de viento zonal que se pudieron atribuir al calentamiento del Ártico. Sin embargo, en contraste con algunos resultados publicados en la literatura que se basan en pruebas de significancia estadística como la prueba de t de Student, en latitudes medias no se encontraron señales en estas dos variables atmosféricas en ese nivel que fueran atribuibles a la reducción del hielo Ártico. En la segunda parte, se analizan las diferencias regionales de la respuesta de la corriente de chorro subtropical (STJ) del Hemisferio Norte a la reducción del hielo Ártico. Los resultados numéricos de esa respuesta mostraron que la STJ se desplazó hacia el sur de la posición climatológica que tiene en las simIn this work, the effect of the sea ice extent reduction of the Arctic Ocean on the mid-latitudes atmospheric circulation of the Northern Hemisphere was studied. Different numerical simulations were performed with a general circulation model (GCM) in which the Arctic sea ice extent was reduced to cause a long-term increase in high latitudes surface temperature similar to that in the instrumental records. Two sets of global simulation experiments with reduced Arctic sea ice extent were made: 1) simulations that, south of 70N, use an annual climatological cycle of sea surface temperature (SST), and 2) the second uses the full SST variability. The analysis focused on the winter season, and the results were divided into two sections. In the first part, two metrics were proposed to identify signals of Arctic warming masked by the internal climate variability (ICV). Metrics based on the Earth Mover’s Distance (EMD) were used to assess the differences between the numerical experiments, and the spatial structure of the atmospheric response to Arctic warming was analyzed with the Structural Similarity Index (SSIM). The standard statistical method used in previous studies to test for significance (the two-tailed Student’s t-test) was found to be prone to false-positive results, which could lead to the erroneous attribution of signals in the modeled response to Arctic warming. The two metrics identified persistent differences at 500hPa in the geopotential height and zonal wind fields attributed to Arctic warming in high latitudes. However, in contrast to some results published in the literature based on statistical significance tests such as the Student’s t-test, at mid-latitudes, no signals were found in these two atmospheric variables at mid-troposphere levels that were attributable to the Arctic sea ice extent reduction. In the second part, regional responses of the subtropical jet stream (STJ) in the Northern Hemisphere to the reduction of Arctic sea ice were analyzed. The numerical results showed that the STJ shifted equatorward from its climatological position in the control simulations over North America, the Atlantic Ocean and Europe, and over the Pacific basin the STJ is driven by the tropical SST above the effects of the Arctic warming
Offshore wind energy climate projection using UPSCALE climate data under the RCP8.5 emission scenario
No full textIn previous work, the authors demonstrated how data from climate simulations can be utilized to estimate regional wind power densities. In particular, it was shown that the quality of wind power densities, estimated from the UPSCALE global dataset in offshore regions of Mexico, compared well with regional high resolution studies. Additionally, a link between surface temperature and moist air density in the estimates was presented. UPSCALE is an acronym for UK on PRACE (the Partnership for Advanced Computing in Europe)-weather-resolving Simulations of Climate for globAL Environmental risk. The UPSCALE experiment was performed in 2012 by NCAS (National Centre for Atmospheric Science)- Climate, at the University of Reading and the UK Met Office Hadley Centre. The study included a 25.6-year, five-member ensemble simulation of the HadGEM3 global atmosphere, at 25km resolution for present climate conditions. The initial conditions for the ensemble runs were taken from consecutive days of a test configuration. In the present paper, the emphasis is placed on the single climate run for a potential future climate scenario in the UPSCALE experiment dataset, using the Representation Concentrations Pathways (RCP) 8.5 climate change scenario. Firstly, some tests were performed to ensure that the results using only one instantiation of the current climate dataset are as robust as possible within the constraints of the available data. In order to achieve this, an artificial time series over a longer sampling period was created. Then, it was shown that these longer time series provided almost the same results than the short ones, thus leading to the argument that the short time series is sufficient to capture the climate. Finally, with the confidence that one instantiation is sufficient, the future climate dataset was analysed to provide, for the first time, a projection of future changes in wind power resources using the UPSCALE dataset. It is hoped that this, in turn, will provide some guidance for wind power developers and policy makers to prepare and adapt for climate change impacts on wind energy production. Although offshore locations around Mexico were used as a case study, the dataset is global and hence the methodology presented can be readily applied at any desired location. © Copyright 2016 Gross, Magar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and repro
Flexible Simulation of Deformable Models Using Discontinuous Galerkin FEM
No full textKaufmann P, Martin S, Botsch M, Gross M. Flexible Simulation of Deformable Models Using Discontinuous Galerkin FEM. In: ACM SIGGRAPH / Eurographics Symposium on Computer Animation. 2008: 105-115
Globally Coupled Collision Handling Using Volume Preserving Impulses
No full textWe present a novel algorithm for collision processing on triangulated meshes. Our method robustly maintains a collision free state on complex geometries while resorting to collision resolution at time intervals often comparable to the frame rate. Our approach is motivated by the behavior of a thin layer of fluid inserted in the empty space between nearly-colliding parts of the simulated surface, acting as a cushioning mechanism. Point-triangle or edge-edge pairs on a collision course are naturally resolved by the incompressible response of this fluid buffer. This response is formulated into a globally coupled nonlinear system which we solve using Newton iteration and symmetric, positive definite solvers. The globally coupled treatment of collisions allows us to resolve up to two orders of magnitude more collisions than traditional greedy algorithms (e.g. Gauss-Seidel collision response) and take substantially larger time steps without compromising the visual quality of the simulation.Eurographics/SIGGRAPH Symposium on Computer Animatio
How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?
No full textMokbel B, Gross S, Lux M, Pinkwart N, Hammer B. How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning? In: Mana N, Schwenker F, Trentin E, eds. Artificial Neural Networks in Pattern Recognition. 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012. Proceedings. Lecture Notes in Artificial Intelligence. Vol 7477. Springer Berlin Heidelberg; 2012: 1-13
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