Limits to Predictability an Nonlinear Scaling in the Atmosphere
Abstract
The long-range aim of this work was to improve our understanding of the atmosphere through the development of new methods for forecasting and for extracting information from raw time series data. The specific aim of this project was to develop and refine nonlinear forecast methods as tools to analyze atmospheric field observations with a view toward the following: (1) to diagnose the underlying dynamic mechanisms driving barometric time series; (2) to determine the effectiveness of nonlinear predictive methods in forecasting atmospheric phenomena; (3) to document and better understand the scale dependence of predictability in atmospheric data.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 19, 1998
- Accession Number
- ADA351464
Entities
People
- George Sugihara
Organizations
- Scripps Institution of Oceanography