Using Lomb–Scargle Analysis to Derive Empirical Orthogonal Functions from Gappy Meteorological Data

Dupuis, Christopher; Schumacher, Courtney

doi:10.1175/JAMC-D-17-0250.1

Title: Using Lomb–Scargle Analysis to Derive Empirical Orthogonal Functions from Gappy Meteorological Data

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Abstract

The Lomb–Scargle discrete Fourier transform (LSDFT) is a well-known technique for analyzing time series. In this study, a solution for empirical orthogonal functions (EOFs) based on irregularly sampled data is derived from the LSDFT. It is demonstrated that this particular algorithm has no hard limit on its accuracy and yields results comparable to those of complex Hilbert EOF analysis. Two LSDFT algorithms are compared in terms of their performance in evaluating EOFs for precipitation observations from the Tropical Rainfall Measuring Mission satellite. Both are shown to be able to capture the pattern of the diurnal cycle of rainfall over the complex topography and diverse land cover of South America, and both also show other consistent features in the 0–12-day frequency band.

Authors:

Dupuis, Christopher ^[1]; Schumacher, Courtney ^[2]

Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
Department of Atmospheric Sciences, Texas A&M University, College Station, Texas

Publication Date:: 2018-10-01

Research Org.:: Texas A & M Univ., College Station, TX (United States)

Sponsoring Org.:: USDOE Office of Science (SC)

OSTI Identifier:: 1471161

Alternate Identifier(s):: OSTI ID: 1612446

Grant/Contract Number:: SC0016245

Resource Type:: Published Article

Journal Name:: Journal of Applied Meteorology and Climatology

Additional Journal Information:: Journal Name: Journal of Applied Meteorology and Climatology Journal Volume: 57 Journal Issue: 10; Journal ID: ISSN 1558-8424

Publisher:: American Meteorological Society

Country of Publication:: United States

Language:: English

Subject:: 54 ENVIRONMENTAL SCIENCES; Meteorology & Atmospheric Sciences; Empirical orthogonal functions; Spectral analysis/models/distribution; Time series

Citation Formats


                    Dupuis, Christopher, and Schumacher, Courtney. Using Lomb–Scargle Analysis to Derive Empirical Orthogonal Functions from Gappy Meteorological Data.  United States: N. p., 2018. 
Web.  doi:10.1175/JAMC-D-17-0250.1.

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                    Dupuis, Christopher, & Schumacher, Courtney. Using Lomb–Scargle Analysis to Derive Empirical Orthogonal Functions from Gappy Meteorological Data.  United States.  https://doi.org/10.1175/JAMC-D-17-0250.1

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                    Dupuis, Christopher, and Schumacher, Courtney. Mon .  
"Using Lomb–Scargle Analysis to Derive Empirical Orthogonal Functions from Gappy Meteorological Data".  United States.  https://doi.org/10.1175/JAMC-D-17-0250.1.

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@article{osti_1471161,

  title        = {Using Lomb–Scargle Analysis to Derive Empirical Orthogonal Functions from Gappy Meteorological Data},

  author       = {Dupuis, Christopher and Schumacher, Courtney},

  abstractNote = {The Lomb–Scargle discrete Fourier transform (LSDFT) is a well-known technique for analyzing time series. In this study, a solution for empirical orthogonal functions (EOFs) based on irregularly sampled data is derived from the LSDFT. It is demonstrated that this particular algorithm has no hard limit on its accuracy and yields results comparable to those of complex Hilbert EOF analysis. Two LSDFT algorithms are compared in terms of their performance in evaluating EOFs for precipitation observations from the Tropical Rainfall Measuring Mission satellite. Both are shown to be able to capture the pattern of the diurnal cycle of rainfall over the complex topography and diverse land cover of South America, and both also show other consistent features in the 0–12-day frequency band.},

  doi          = {10.1175/JAMC-D-17-0250.1},

  journal      = {Journal of Applied Meteorology and Climatology},

  number       = 10,

  volume       = 57,

  place        = {United States},

  year         = {2018},

  month        = {10}

}

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