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Activity Number:
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75
- Invited EPoster Session II
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Type:
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Invited
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Date/Time:
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Sunday, August 7, 2022 : 9:35 PM to 10:30 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #323028
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Title:
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A Graphical Lasso Model for Hermitian Matrices to Detect Global Time-Lagged Teleconnections
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Author(s):
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Indranil Sahoo* and Joe Guinness and Brian James Reich
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Companies:
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Virginia Commonwealth University and Cornell University and North Carolina State University
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Keywords:
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spatiotemporal data;
climate models;
Graphical LASSO;
inverse covariance;
spherical needlets;
FFT
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Abstract:
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Teleconnections refer to spatially and temporally connected large-scale anomalies that influence the variability of atmospheric phenomena. Since teleconnections influence the global climate system, it is important to understand the abnormal behavior and interactions of these phenomena and identify them accurately. In this paper, we provide a mathematical definition of teleconnections based on a spatio-temporal model using spherical needlet functions. Spherical needlets are exactly localized at several overlapping intervals corresponding to different frequencies in the frequency domain and form a tight frame. This ensures the perfect reconstruction property of an orthonormal basis. We extend the famous graphical Lasso algorithm to incorporate Hermitian matrices and use it to detect teleconnections by estimating the inverse covariance matrix of needlet coefficients after projecting them onto the Fourier domain. The proposed method is demonstrated by simulation studies and detection of possible global teleconnections in the HadCM3 model output air temperature data.
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Authors who are presenting talks have a * after their name.
