Self-similarity patterns of precipitation in the Iberian Peninsula

Summary An objective classification of the precipitation field over the Iberian Peninsula and the Balearic Islands is obtained. Data are derived from a high-resolution daily precipitation dataset obtained from in-situ measurements. The dataset, Iberian monthly Precipitation Dataset (IPD), consists of monthly precipitation data over a 25 km × 25 km grid from 1^st January 1961 to 31^st December 2003. Therefore, 960 data series over the Iberian Peninsula and the Balearic Islands are disposed over the grid for 43-year period. Multi-resolution wavelet analysis is used to extract similar information in the precipitation field at different timescales. An objective classification of the obtained wavelet coefficient series is carried out by means of the Kohonen’s neural network, also named Self-Organizing Map (SOM). SOM is formed by an unsupervised learning algorithm that may be used to find clusters of similar events in the input data and is able to identify some underlying dynamic structures of the multi-dimensional datasets. SOM is applied to the wavelet coefficients for intramonthly, intermonthly and interannual oscillations, obtaining self-organised maps which objectively identify similar zones of precipitation behaviour over the Iberian Peninsula. The homogeneity of the patterns is also studied by means of non-parametric correlations, energy scalograms and tests of significance. The intramonthly, intermonthly and interannual waves resulted in seven, five and three SOM patterns, respectively. As timescale increases, the wavelet series coefficients tend to be highly clustered. The results indicate that as the oscillation frequencies decrease, the Iberian precipitation behaves more linearly.