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A data-driven method for extracting temporally persistent information, at different spatial scales, from 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 decompose spatial rainfall data into a sequence of high through to low frequency components. This process is equivalent to the application of successive low-pass spatial filters, but based on the observed propertiesdoi:10.5194/hess-9-127-2005 fatcat:wzapcvkn5fdwzoflc7uszlh6je