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D. D. Cyr, “A Spline Kernel Based Smoothing Algorithm: A Comparison of Methods and a Spatiotemporal Application to Global Climate Fluctuations,” Ph.D. Thesis, State University of New York, Albany, 2010.

has been cited by the following article:

• TITLE: Restoration of Time-Spatial Scales in Global Temperature Data

  AUTHORS: Igor Zurbenko, Ming Luo

  KEYWORDS: Separation of Scales; Kolmogorov-Zurbenko Filtration in Time and Space; Climate Variability; El Niño-like Movement; Global Long Term Trend; Spectral Analysis; Correlation Analysis; Temporal-Spatial Data

  JOURNAL NAME: American Journal of Climate Change, Vol.1 No.3, September 20, 2012

  ABSTRACT: The objective of this paper is to utilize images of spatial and temporal fluctuations of temperature over the Earth to study the global climate variation. We illustrated that monthly temperature observations from weather stations could be decomposed as components with different time scales based on their spectral distribution. Kolmogorov-Zurbenko (KZ) filters were applied to smooth and interpolate gridded temperature data to construct global maps for long-term (≥ 6 years) trends and El Nino-like (2 to 5 years) movements over the time period of 1893 to 2008. Annual temperature seasonality, latitude and altitude effects have been carefully accounted for to capture meaningful spatiotemporal patterns of climate variability. The result revealed striking facts about global temperature anomalies for specific regions. Correlation analysis and the movie of thermal maps for El Nino-like component clearly supported the existence of such climate fluctuations in time and space.