We present an application of Self-Organizing Maps (SOM) for analyzing multi-model ensemble seasonal forecasts from the DEMETER project in the tropical area of Northern Peru. SOM is an automatic data-mining clustering technique which allows summarizing a high-dimensional data space in terms of a set of reference vectors (cluster centroids). Moreover, it has outstanding analysis and visualization properties, since the reference vectors can be projected into a 2D lattice preserving their high-dimensional
In the first part of the paper, SOM is applied to analyze both atmospheric patterns over Peru and local precipitation observations at two nearby stations. First, the method is applied to cluster the ERA40 reanalysis patterns on the area of study (Northern Peru), obtaining a 2D lattice which represents the climatology. Then, each particular phenomenon or event (e.g., El Niño or La Niña) is shown to define a Probability Density Function (PDF) on the lattice, which represents its characteristic "location" within the climatology. On the other hand, the climatological lattice is also used to represent the local precipitation regime associated with a given station. For instance, we show that the precipitation regime is strongly associated with El Niño events for one station, whereas it is more uniform for the other.
The second part of the paper is devoted to downscaling seasonal ensemble forecasts from the multi-model DEMETER ensemble to local stations. To this aim, the PDF generated on the lattice by the patterns predicted for a particular season is combined with the local precipitation lattice for a given station. Thus, a probabilistic or numeric local forecast is easily obtained from the resulting PDF. Moreover, a measure of predictability for the downscaled forecast can be computed in terms of the entropy of the ensemble PDF. We present some evidence that accurate local predictions for accumulated seasonal precipitation can be obtained some months in advance for strong El Niño episodes. Finally, we compare the multi-model ensemble with single-model ensembles, and show that the best results correspond to different models for different years; however, the best global performance over the whole period corresponds to the multi-model ensemble.