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Hansen, J.A. (2002) Accounting for Model Error in Ensemble-Based State Estimation and Forecasting, Monthly Weather Review, 130, 2373-2391.
https://doi.org/10.1175/1520-0493(2002)130<2373:AFMEIE>2.0.CO;2

has been cited by the following article:

  • TITLE: High Dimensional Cluster Analysis Using Path Lengths

    AUTHORS: Kevin Mcilhany, Stephen Wiggins

    KEYWORDS: Clustering, Path Length, Consensus, N-Dimensional, Line of Sight

    JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.6 No.3, July 10, 2018

    ABSTRACT: A hierarchical scheme for clustering data is presented which applies to spaces with a high number of dimensions (). The data set is first reduced to a smaller set of partitions (multi-dimensional bins). Multiple clustering techniques are used, including spectral clustering; however, new techniques are also introduced based on the path length between partitions that are connected to one another. A Line-of-Sight algorithm is also developed for clustering. A test bank of 12 data sets with varying properties is used to expose the strengths and weaknesses of each technique. Finally, a robust clustering technique is discussed based on reaching a consensus among the multiple approaches, overcoming the weaknesses found individually.