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The identification of the characteristics of short time rainstorms in urban areas is a difficult problem. The traditional rainfall definition methods, using rainfall graph or a GIS map, respectively reflect the temporal or spatial variations of a rainfall process, but do not regard a rainfall as one complete process including its temporal and spatial dimension. In this paper, we present an approach to define typical modes of rainfall from the temporal and spatial dimensions. Firstly, independent rainfall processes are divided based on the continuous monitoring data of multiple rainfall stations. Subsequently, algorithms are applied to identify the typical spatiotemporal modes of rainfall and reconstruction of the process of modes, including dimensionality reduction, clustering, and reconstruction. This approach is used to analyze the monitoring data (5 min intervals) from 2004 to 2016 of 14 rainfall stations in Beijing. The results show that there are three modes of rainstorms in the Beijing urban area, which account for 31.8%, 13.7%, and 54.6% of the total processes. Rainstorm of mode 1 moves from the northwest to the center of Beijing, then spreads to the eastern part of the urban area; rainstorm of mode 2 occurs in the southwestern region of the urban area, and gradually northward, but there is no rainfall in the mountainous northwest; rainstorm of mode 3 is concentrated in the central, eastern, and southern regions. The approach and results of this study can be applied to rainstorm forecasting or flood prevention.

Spatiotemporal Modes of Short Time Rainstorms Based on High-Dimensional Data: A Case Study of the Urban Area of Beijing, China