Using Lomb–Scargle Analysis to Derive Empirical Orthogonal Functions from Gappy Meteorological Data
- Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey
- Department of Atmospheric Sciences, Texas A&M University, College Station, Texas
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.
- Research Organization:
- Texas A & M Univ., College Station, TX (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- Grant/Contract Number:
- SC0016245
- OSTI ID:
- 1471161
- Journal Information:
- Journal of Applied Meteorology and Climatology, Journal Name: Journal of Applied Meteorology and Climatology Journal Issue: 10 Vol. 57; ISSN 1558-8424
- Publisher:
- American Meteorological SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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