In this paper an application of clustering algorithms for statistical downscaling in short-range weather forecasts is presented. The advantages of this technique compared with standard nearest-neighbors analog methods are described both in terms of computational efficiency and forecast skill. Some validation results of daily precipitation and maximum wind speed operative downscaling (lead time 1-5 days) on a network of 100 stations in the Iberian Peninsula are reported for the period 1998-99. These results indicate that the weighting clustering method introduced in this paper clearly outperforms standard analog techniques for infrequent, or extreme, events (precipitation 20 mm; wind 80 km h 1). Outputs of an operative circulation model on different local-area or large-scale grids are considered to characterize the atmospheric circulation patterns, and the skill of both alternatives is compared.

We present an application of self-organizing maps (SOMs) for analysing multi-model ensemble seasonal forecasts from the DEMETER project in the tropical area of Northern Peru. The SOM is an automatic data-mining clustering technique, which allows us to summarize a high-dimensional data space in terms of a set of reference vectors (cluster centroids). Moreover, it has outstanding analysis and visualization properties, because the reference vectors can be projected into a two-dimensional lattice, preserving their high-dimensional topology. In the first part of the paper, the SOM is applied to analyse 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 two-dimensional 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.

We present an application of self‐organizing maps (SOMs) for analysing multi‐model ensemble seasonal forecasts from the DEMETER project in the tropical area of Northern Peru. The SOM is an automatic data‐mining clustering technique, which allows us to summarize a high‐dimensional data space in terms of a set of reference vectors (cluster centroids). Moreover, it has outstanding analysis and visualization properties, because the reference vectors can be projected into a two‐dimensional lattice, preserving their high‐dimensional topology. In the first part of the paper, the SOM is applied to analyse 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 two‐dimensional 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.

Abstract. In this paper we present some applications of Bayesian networks in Meteorology from a data mining point of view. We work with a database of observations (daily rainfall and maximum wind speed) in a network of 100 stations in the Iberian peninsula and with the corresponding gridded atmospheric patterns generated by a numerical circulation model. As a first step, we analyze the efficiency of standard learning algorithms to obtain directed acyclic graphs representing the spatial dependencies among the variables included in the database; we also present a new local learning algorithm which takes advantage of the spatial character of the problem. The resulting graphical models are applied to different meteorological problems including weather forecast and stochastic weather generation. Some promising results are reported.

k.a.buchin | b.speckmann]@tue.nl, [t.h.a.castermans | a.pieterse | w.m.sonke]@student.tue.nl.Figure 1: US election 2012, electorial college votes. Diffusion cartogram by Mark Newman (University of Michigan), square mosaic cartogram from Wikipedia, square and hexagonal mosaic cartograms computed by our algorithm. AbstractCartograms visualize quantitative data about a set of regions such as countries or states. There are several different types of cartograms and -for some -algorithms to automatically construct them exist. We focus on mosaic cartograms: cartograms that use multiples of simple tiles -usually squares or hexagons -to represent regions. Mosaic cartograms communicate well data that consist of, or can be cast into, small integer units (for example, electorial college votes). In addition, they allow users to accurately compare regions and can often maintain a (schematized) version of the input regions' shapes. We propose the first fully automated method to construct mosaic cartograms. To do so, we first introduce mosaic drawings of triangulated planar graphs. We then show how to modify mosaic drawings into mosaic cartograms with low cartographic error while maintaining correct adjacencies between regions. We validate our approach experimentally and compare to other cartogram methods.