Empirical Mode Decomposition in 2-D space and time: a tool for space-time rainfall analysis and nowcasting

S. Sinclair (1) , G. G. S. Pegram (1)

1 Civil Engineering

A data-driven method for extracting information, at temporally predictable scales, from spatial rainfall data (as measured by radar/satellite) is described, which extends the Empirical Mode Decomposition (EMD) algorithm into two dimensions. The EMD technique is used here to separate spatial rainfall data into a sequence of high through to low frequency components. This process is equivalent to a low-pass spatial filter, but based on the observed properties of the data rather than the predefined basis functions used in traditional Fourier or Wavelet decompositions. It has been suggested in the literature that the lower frequency components of spatial rainfall data exhibit greater temporal persistence than the higher frequency ones. This idea is explored here in the context of Empirical Mode Decomposition, to prepare rainfall data for nowcasts based on the temporal evolution of the lower frequency components. The paper focuses on the implementation and development of the two-dimensional extension to the EMD algorithm and it's application to radar rainfall data, as well as examining temporal persistence in the data at different spatial scales.

Domaines: Interfaces continentales, environnement; Océan, Atmosphère; Sciences de la Terre

S. Sinclair, G. G. S. Pegram. Empirical Mode Decomposition in 2-D space and time: a tool for space-time rainfall analysis and nowcasting. Hydrology and Earth System Sciences Discussions, 2005, 2 (1), pp.289-318. ⟨hal-00298627⟩