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Analysis of Climate Change Signals

The assessment and detection of global warming is of great concern to the world now. This project aimed to detect quantitative characteristics of observed climate changes and to develop a new mathematical theory for analyzing non-stationary and nonlinear remote-sensing signals. A key outcome was a master dataset for the latest version of the Alberta Agroclimatic Atlas. The dataset includes both digital data and map images. The agroclimatic changes over Alberta since 1901 to 2002 were quantitatively documented and the analysis shows that the Alberta agriculture has benefited from the changes. In addition, the team discovered internal upstream running solitons from satellite images over clouds, which can be used to predict severe weather. A major theoretical result was the establishment of the statistical confidence limit for the Hilbert-Huang Transform for analyzing nonlinear and non-stationary time series. An example was analyzed for the length-of-day data and the El Niño signals were detected from the data. All the results above have been published in refereed journals or books. This project was completed in 2004.

Original Research Category: 
Project Leader(s): 

Samuel Shen, Alberta