An objective separation of rainfall classes in the high tropical Andes by using a clustering analysis.

Information about the temporal rainfall variability at high-resolution is scarce, especially in regions with complex topography as the Tropical Andes, and this hinders the study rainfall dynamics. The identification of rainfall types is usually determined using thresholds of some rainfall characteristics as rain rate and velocity. Nevertheless, these thresholds are identified for a specific study area and thus they cannot be extrapolated to other places to identify rainfall classes. Thus, the aim of this study is to investigate rainfall-event classes based on a clustering approach by using the k-means algorithm. The clustering analysis is used to group objects (i.e., rainfall-events) based on its characteristics (e.g., duration, intensity, drop size distribution, melting layer identification). This study was carried out using data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The instruments were located in the tropical Andes, at 2600 m a.s.l., in the city of Cuenca, Ecuador.  Three years of data were available for the study. Firstly, the rainfall events were selected by using the criteria: minimum inter-event, minimum total accumulation and minimum duration. Then, by using the k-means algorithm, two principal rainfall classes were identified in the study area. These rainfall classes (i.e., convective, stratiform) showed marked differences in their rainfall characteristics. Besides, a third rainfall class (mixed class) was identified as a subclass of the stratiform class. The stratiform class was more common during the year in the study area. Also, short duration rainfall events (less than 70 min) were dominant. Furthermore, the melting layer characteristic – that is used to determine rainfall classes – did not influence the rainfall class identification using the clustering analysis, especially in two classes; thus, its prior study is not necessary, and this makes the clustering analysis highly beneficial. Finally, this clustering analysis ensured an objective separation of rainfall classes in the tropical high Andes. This rainfall classification provided new insights about the rainfall dynamics in this tropical mountain area.