Contrasting Climate Ensembles: A Model-Based Visualization Approach for Analyzing Extreme Events

被引:6
|
作者
Sisneros, Robert [1 ]
Huang, Jian [2 ]
Ostrouchov, George [3 ]
Ahern, Sean [3 ]
Semeraro, B. David [1 ]
机构
[1] Univ Illinois, Natl Ctr Supercomp Applicat, Urbana, IL 61801 USA
[2] Univ Tennessee, Knoxville, TN 37996 USA
[3] Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA
关键词
visualization; climate ensembles; multivariate classification; PROJECTIONS;
D O I
10.1016/j.procs.2013.05.406
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The use of increasingly sophisticated means to simulate and observe natural phenomena has led to the production of larger and more complex data. As the size and complexity of this data increases, the task of data analysis becomes more challenging. Determining complex relationships among variables requires new algorithm development. Addressing the challenge of handling large data necessitates that algorithm implementations target high performance computing platforms. In this work we present a technique that allows a user to study the interactions among multiple variables in the same spatial extents as the underlying data. The technique is implemented in an existing parallel analysis and visualization framework in order that it be applicable to the largest datasets. The foundation of our approach is to classify data points via inclusion in, or distance to, multivariate representations of relationships among a subset of the variables of a dataset. We abstract the space in which inclusion is calculated and through various space transformations we alleviate the necessity to consider variables' scales and distributions when making comparisons. We apply this approach to the problem of highlighting variations in climate model ensembles.
引用
收藏
页码:2347 / 2356
页数:10
相关论文
共 50 条
  • [31] Modelling the Climate System: Is Model-Based Science Like Model-Based Engineering?
    Easterbrook, Steve
    2015 ACM/IEEE 18TH INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS (MODELS), 2015, : 1 - 1
  • [32] Reconstruction and visualization of model-based volume representations
    Zheng, Ziyi
    Mueller, Klaus
    MEDICAL IMAGING 2010: VISUALIZATION, IMAGE-GUIDED PROCEDURES, AND MODELING, 2010, 7625
  • [33] The Effects of Model Resolution on the Simulation of Regional Climate Extreme Events
    汤剑平
    赵鸣
    苏炳凯
    Acta Meteorologica Sinica, 2007, (02) : 129 - 140
  • [34] A chaotically driven model climate: extreme events and snapshot attractors
    Bodai, T.
    Karolyi, Gy.
    Tel, T.
    NONLINEAR PROCESSES IN GEOPHYSICS, 2011, 18 (05) : 573 - 580
  • [35] The effects of model resolution on the simulation of regional climate extreme events
    Tang Jianping
    Zhao Ming
    Su Bingkai
    ACTA METEOROLOGICA SINICA, 2007, 21 (02): : 129 - 140
  • [36] A MODEL-BASED ANOMALY DETECTION APPROACH FOR ANALYZING STREAMING AIRCRAFT ENGINE MEASUREMENT DATA
    Simon, Donald L.
    Rinehart, Aidan W.
    PROCEEDINGS OF THE ASME TURBO EXPO: TURBINE TECHNICAL CONFERENCE AND EXPOSITION, 2014, VOL 6, 2014,
  • [37] Optimally Weighted Ensembles in Model-Based Regression for Drug Discovery
    Echtenbruck, Patrick
    Emmerich, Michael
    Echtenbruck, Martina
    Naujoks, Boris
    2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021), 2021, : 2251 - 2258
  • [38] Discussion of “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach” by Huang Huang, Stefano Castruccio, Allison H. Baker and Marc Genton
    Sudipto Banerjee
    Journal of Agricultural, Biological and Environmental Statistics, 2023, 28 : 365 - 369
  • [39] Discussion of "Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach" by Huang Huang, Stefano Castruccio, Allison H. Baker and Marc Genton
    Banerjee, Sudipto
    JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 2023, 28 (02) : 365 - 369
  • [40] A Model-Based Approach to Climate Reconstruction Using Tree-Ring Data
    Schofield, Matthew R.
    Barker, Richard J.
    Gelman, Andrew
    Cook, Edward R.
    Briffa, Keith R.
    JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2016, 111 (513) : 93 - 106