Abstract
Abstract. Rain time series records are generally studied using rainfall rate or accumulation parameters, which are estimated for a fixed duration (typically 1 min, 1 h or 1 day). In this study we use the concept of rain events. The aim of the first part of this paper is to establish a parsimonious characterization of rain events, using a minimal set of variables selected among those normally used for the characterization of these events. A methodology is proposed, based on the combined use of a genetic algorithm (GA) and self-organizing maps (SOMs). It can be advantageous to use an SOM, since it allows a high-dimensional data space to be mapped onto a two-dimensional space while preserving, in an unsupervised manner, most of the information contained in the initial space topology. The 2-D maps obtained in this way allow the relationships between variables to be determined and redundant variables to be removed, thus leading to a minimal subset of variables. We verify that such 2-D maps make it possible to determine the characteristics of all events, on the basis of only five features (the event duration, the peak rain rate, the rain event depth, the standard deviation of the rain rate event and the absolute rain rate variation of the order of 0.5). From this minimal subset of variables, hierarchical cluster analyses were carried out. We show that clustering into two classes allows the conventional convective and stratiform classes to be determined, whereas classification into five classes allows this convective–stratiform classification to be further refined. Finally, our study made it possible to reveal the presence of some specific relationships between these five classes and the microphysics of their associated rain events.
Highlights
The analysis of “precipitation events” or “rain events” can be used to obtain information concerning the characteristics of precipitation at a particular location and for a specific application
The definition of a rain event is relatively subjective, this study underlines the advantages of using event analysis rather than sample analysis
This data-driven analysis of events shows that rain events exhibit coherent features
Summary
The analysis of “precipitation events” or “rain events” can be used to obtain information concerning the characteristics of precipitation at a particular location and for a specific application. This is a convenient way to summarize precipitation time series in the form of a small number of characteristics that make sense for particular applications. When a rain rate time series (generally based on rain gauge records) is broken down into individual rainfall events, a wide variety of their characteristics, such as average rainfall rate, rain event duration and rainfall event distribution (known as hydrological information), can be computed for each event. The first goal of the present study is to select a reduced set of features characterizing rainfall events, through the use of a data-driven approach, without taking a priori knowledge of the field of application into account, thereby characterizing rainfall events in the most parsimonious and efficient manner
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