4  Correlation and dimensionality 🚧

Learning objectives

After reading this chapter, you should be able to:

  • Model and interpret spatial dependence in climate fields
  • Apply time series analysis methods to detect trends and patterns in climate data
  • Use dimension reduction techniques for high-dimensional climate datasets
  • Integrate spatial and temporal methods for spatiotemporal climate analysis

4.1 Essential concepts

  1. Spatial statistics and geostatistical methods
  2. Time series analysis and trend detection
  3. Principal component analysis and empirical orthogonal functions
  4. High-dimensional methods for climate data
  5. Spatiotemporal integration approaches

Further reading

For spatial and temporal analysis in climate science:

  • Cressie and Wikle (2011): Comprehensive treatment of spatial statistics