Use of self organizing map to identify precipitation patterns and assess their impact on hydrographic basins in Brazil

Abstract

ABSTRACT In this study, we used neural networks known as self-organizing maps (SOMs) to identify clusters of spatial synoptic precipitation patterns. These clusters were compared with the precipitation regime of the ten main hydrographic sub-basins in Brazil. Sixty years of daily precipitation data obtained from over 389 weather station in Brazil were used as input data for the SOMs, with a number of six clusters being prescribed as the optimal number according to the elbow and silhouette methods. The six precipitation patterns identified by the SOMs reflect the typical synoptic conditions associated mainly with the cold frontal systems (CF), South American Monsoon System (SAMS) and Inter-tropical Convergence Zone (ITCZ). In conclusion, SOMs perform well using interpolated precipitation data as the input data to identify synoptic precipitation patterns, which could be used to monitor the spatial distribution of precipitation, which affects the hydrographic basins in Brazil and hence hydropower plant performance.

Keywords

• Self-organizing map
• Precipitation regime
• Spatial-Temporal Pattern