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A visual uncertainty analytics approach for weather forecast similarity measurement based on fuzzy clustering

机译：基于模糊聚类的天气预报相似性测量视觉不确定性分析方法

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摘要

Forecast calibration methods based on historical similar atmospheric state are effective means weather forecast accuracy. Conventional approaches search similar forecasts on the basis of predefined similarity formulas and provide calibration recommendations to forecasters. However, these approaches ignore the uncertainty of similarity measurement, which affects calibration efficacy significantly. This study proposes a similarity weight adaptive algorithm for high-dimensional data on the basis of fuzzy clustering to characterize the uncertainty of similarity measurements. Without prior knowledge, the algorithm computes the uncertainty of the similarity between data in the fuzzy set space iteratively on the basis of membership and then determine weight distribution by maximizing the differentiating ability of each dimension. This study further presents a visual analysis framework on the basis of the weight adaptive algorithm for the exploration of uncertainty in meteorological data and the optimization of similarity measurement method. This framework has coordinated views and intuitive interactions to enable the visualization of the similarity uncertainty distribution and support the iterative visual analysis of similarity weight distribution in each dimension that combines domain knowledge. We illustrate a case study using real-world meteorological data to verify the efficacy of the proposed approach.
机译：基于历史类似大气状态的预测校准方法是有效的，是天气预报准确性的有效意味着天气预报。传统方法在预定义的相似式公式的基础上搜索类似的预测，并为预测者提供校准建议。然而，这些方法忽略了相似度测量的不确定性，这显着影响了校准效能。本研究提出了基于模糊聚类的高维数据的相似性重量自适应算法，以表征相似度测量的不确定性。在没有先验知识的情况下，该算法基于成员资格迭代地计算模糊集空间中数据之间的相似性的不确定性，然后通过最大化每个维度的差异化能力来确定权重分布。本研究进一步介绍了用于探索气象数据中的不确定性的重量自适应算法和相似性测量方法的优化。该框架具有协调的视图和直观的交互，以实现相似性不确定性分布的可视化，并支持组合域知识的每个维度中相似性权重分布的迭代视觉分析。我们说明了使用现实世界气象数据来验证所提出的方法的功效的案例研究。

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  • 来源
    《Journal of visualization》 |2021年第2期|317-330|共14页
  • 作者

    Renpei Huang; Li Chen; Xiaoru Yuan;

  • 作者单位

    School of Software BNRIST Tsinghua University Beijing People's Republic of China;

    School of Software BNRIST Tsinghua University Beijing People's Republic of China;

    Key Laboratory of Machine Perception (Ministry of Education) and National Engineering Laboratory for Big Data Analysis and Application Peking University Beijing People's Republic of China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Uncertainty visualization; Fuzzy clustering; Weather forecast;

    机译：不确定性可视化;模糊聚类;天气预报;

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