Synoptic Classification and Establishment of Analogues with Artificial Neural Networks

Weather charts depicting the spatial distribution of various meteorological parameters constitute an indispensable pictorial tool for meteorologists, in diagnosing and forecasting synoptic conditions and the associated weather. The purpose of the present research is to investigate whether training artificial neural networks can be employed in the objective identification of synoptic patterns on weather charts. In order to achieve this, the daily analyses at 0000UTC for 1996 were employed. The respective data consist of the grid-point values of the geopotential height of the 500 hPa isobaric level in the atmosphere. A uniform grid-point spacing of 2.5° × 2.5° is used and the geographical area covered by the investigation lies between 25°N and 65°N and between 20°W and 50°E, covering Europe, the Middle East and the Northern African Coast. An unsupervised learning self-organizing feature map algorithm, namely the Kohonen's algorithm, was employed. The input consists of the grid-point data described above and the output is the synoptic class which each day belongs to. The results referred to in this study employ the generation of 15 and 20 synoptic classes (more classes have been investigated but the results are not reported here). The results indicate that the present technique produced a satisfactory classification of the synoptic patterns over the geographical region mentioned above. Also, it is revealed that the classification performed in this study exhibits a strong seasonal relationship.

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