Space-Time ARMA Models for Satellite Ozone Data

Xufeng Niu and Michael Stein
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Xufeng Niu: University of Chicago, Department of Statistics
Michael Stein: University of Chicago, Department of Statistics

A chapter in Computing Science and Statistics, 1992, pp 225-234 from Springer

Abstract: Abstract In this paper, we develop some space-time autoregressive moving-average (ARMA) models for satellite ozone data, in which both the geographic neighbor effects and time-lag effects are considered. For the global TOMS ozone data over the 11-year period from January 1979 to December 1989, we divide the whole world into 10° latitude by 10° longitude blocks and calculate the monthly averages of total ozone for each geographic block. The space-time ARMA models then are applied to the monthly average series to assess the long-term changes in ozone concentrations. The long-term trend estimates over the world are all negative with the most negative trends occurring in the south polar latitudes, which is a reflection of the springtime Antarctic ozone hole that developed over the last several decades.

Keywords: Total Ozone; Total Column Ozone; Trend Estimate; Ozone Data; Simple Regression Model (search for similar items in EconPapers)
Date: 1992
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-2856-1_29

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DOI: 10.1007/978-1-4612-2856-1_29

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