# A Process-Oriented Method for Tracking Rainstorms with a Time-Series of Raster Datasets
## 1 Introduction  
With global warming, extreme rainstorm events tend to occur more frequently than ever [1], which may cause pluvial flooding and potentially severe socioeconomic consequences. Knowing when, where, and how rainstorm events begin and evolve is of paramount importance for science and society [2]. The development of advanced remote sensing technologies makes it possible to monitor and track these phenomena at a large scale [3; 4]. However, observing the fine spatial patterns and structures and temporal evolution of rainstorms from time-series of remote sensing products still remains a challenge [5]. Generally, a rainstorm is defined as intense precipitation averaged over a time period and over a geographic region [6; 7]. Characterizing rainstorms from raster datasets involves two steps:  
* To identify a rainstorm object and determine its spatial location and structure at each time snapshot. * To track the location and intensity of rainstorm objects to determine rainstorm evolution. The general idea of the first point is to define a rainstorm object as a contiguous region of a set of connected pixels that exceed a specified threshold. The Thunderstorm Identification, Tracking,Analysis and Nowcasting (TITAN) algorithm, a well-known object-based algorithm, used a specified single reflectivity threshold value to identify contiguous regions as rainstorm entities [8,9]. The hard single threshold is subject to one problem, that is, it is not possible to distinguish between noisy points and initiating rainstorm cells [6,10]. To address this problem, researchers have applied multiple thresholds [5,6,11,12] and combined thresholds with spatial and temporal features [7,13] to distinguish different types of rainstorm objects. Whether a single threshold or multiple ones are used, rainstorm identification is subject to seasonal, regional, and climatological variabilities [6]. To help mitigate this problem, the watershed transform algorithm has been widely used in many studies [6,10,14]. Rainstorm tracking algorithms link rainstorm objects among successive snapshots. A key factor in such algorithms is which criteria are selected to determine whether rainstorm objects are the same or not. The TITAN algorithm [8] uses a spatial overlapping threshold to determine whether a rainstorm object in snapshot T+1 is the same as the one in snapshot T. The Hungarian algorithm considers the geometrical features and moving distance among rainstorm objects in two successive snapshots. However, as it largely relies on the overlapping areas between successive rainstorm objects, TITAN has difficulty in effectively tracking fast-moving rainstorm objects [5,15]. To address these deficiencies, a number of modifications have been proposed. For example, [PERSON] et al. [16] replaced an overlapping area with a centroid distance to determine whether rainstorm objects belong to the same trajectory or not. Additionally, [PERSON] et al. [12] simultaneously considered the topological overlap, centroid distance, and movement direction of rainstorm objects to design the criterion for classification as rainstorm objects. A region tree structure was first used to represent the rainstorm objects at different scales, and an area-overlapping-based method was then used to match the rainstorm objects in successive snapshots [17]. Another type of rainstorm tracking algorithm is a cross-correlation algorithm, which calculates a motion vector field and forecasts the movement of rainstorm objects [18]. For each rainstorm object, a single movement vector can be replaced by averaging the mapped movement vectors obtained from the field tracker; then, the averaged movement vector is used to extrapolate the rainstorm object in the next snapshot, which is limited by the selected matching criterion [5]. Thus, [PERSON] et al. [5] adopted an object-based method to calculate a number of measurement indexes, including a distance component, an area component, a structural component, and an eccentricity component.