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- Publisher
- Springer International Publishing
- Copyright
- © Springer Nature Switzerland AG 2019
- ISBN
- 978-3-030-22043-3
- Pages
- 55 –74
- DOI
- 10.1007/978-3-030-22044-0_3
- Publisher site
- See Chapter on Publisher Site
Abstract
[This chapter addresses some of the mathematical challenges associated with current experimental and computational methods to analyze spatiotemporal precipitation patterns. After a brief overview of the various methods to measure precipitation from in situ observations, satellite platforms, and via model simulations, the chapter focuses on the statistical assumptions underlying the most common spatiotemporal and pattern-recognition techniques: stationarity, isotropy, and ergodicity. As the variability of Earth’s climate increases and the volume of observational data keeps growing, these assumptions may no longer be satisfied, and new mathematical methodologies may be required. The chapter discusses spatiotemporal decorrelation measures, a nonstationary intensity-duration-function, and 2-dimension reduction methodologies to address these challenges.]
Published: Nov 2, 2019
Keywords: Centroidal Voronoi tessellation; Data reduction; Decorrelation; Empirical orthogonality functions; Ergodicity; Isotropy; Precipitation patterns; Stationarity; Statistical assumptions