Articles | Volume 10, issue 4
Research article
25 Apr 2017
Research article |  | 25 Apr 2017

Data-driven clustering of rain events: microphysics information derived from macro-scale observations

Mohamed Djallel Dilmi, Cécile Mallet, Laurent Barthes, and Aymeric Chazottes

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.

Short summary
The concept of a rain event is used to obtain a parsimonious characterisation of rain events using a minimal subset of variables at macrophysical scale. A classification in five classes is obtained in a unsupervised way from this subset. Relationships between these classes of microphysical parameters of precipitation are highlighted. There are several implications especially for remote sensing in the context of weather radar applications and quantitative precipitation estimation.