Abstract
This study proposes a new algorithm termed rain cell identification and tracking (RCIT) to identify and track rain cells from high resolution weather radar data. Previous algorithms have limitations when tracking non-consequent rain cells owing to their use of maximum correlation coefficient methods and their lack of an alternative way to handle the variation stages of rain cells during their life cycles. To address these deficiencies, various methods are implemented in the new algorithm. These include the particle image velocimetry (PIV) method for motion estimation and the rain cell matching rule to obtain the stage changes of rain cells. High resolution (5 min and 1 km) radar data from three rainy days over the German federal state North Rhine Westphalia (NRW) are used in this study. The performance of the identification module for the new algorithm is accessed by two object-oriented verification methods: structure–amplitude–location (SAL) and geometric index, while the performance of the tracking module is compared with TREC and SCOUT tracking algorithms and evaluated by the contingency table verification approach. Results suggest that the performance of the new algorithm is better than reference tracking method. Application of the RCIT algorithm to the selected cases shows that the inner structure of rainfall events in the experimental region present extreme value distributions, with most rainfall events having a short duration with less intensity. The new algorithm can effectively capture the stage changes of rain cells during their life cycles. The proposed algorithm can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and short-term flood forecasting.
Highlights
Precipitation is a key process in Earth’s water circle
Algorithm to the selected cases shows that the inner structure of rainfall events in the experimental region present extreme value distributions, with most rainfall events having a short duration with less intensity
There were 10,346 rain cells identified from the selected radar reflectivity images
Summary
Precipitation is a key process in Earth’s water circle. Acquiring explicit knowledge about its inner behavior is critical to assisting us in understanding its interaction with hydrological processes.Rainfall events are characterized by several elements, such as duration, intensity, velocity, and spatial and temporal variability [1]. Precipitation is a key process in Earth’s water circle. Acquiring explicit knowledge about its inner behavior is critical to assisting us in understanding its interaction with hydrological processes. Rainfall events are characterized by several elements, such as duration, intensity, velocity, and spatial and temporal variability [1]. The variability of rainfall events can be defined as “the variability derived from having multiple spatially-distributed rainfall fields for a given point in time” [2]. In hydro-meteorological applications, elements of rainfall events (e.g., duration, covering area, intensity, velocity) always vary over the life cycle of events. Modeling rainfall events and analyzing their spatial and temporal information is necessary, in order to improve the accuracy of short-term rainfall forecasting and to lower the uncertainties of hydrological modeling from the variability of rainfall inputs
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