# A Process-Oriented Method for Tracking Rainstorms with a Time-Series of Raster Datasets
## 5 Conclusions  
A new methodology for identifying and tracking rainstorms, called PoAIR, has been presented using long-term raster datasets. PoAIR overcomes two main issues of existing tracking algorithms: (1) It matches rainstorm objects that move at high speed across successive snapshots, and (2) it resolves deficiencies of splitting and merging. For the first issue, PoAIR combines geometric similarities and thematic evolutions as a cost function to track rainstorm objects across successive snapshots; for the second, PoAIR extrapolates the spatial locations of rainstorm objects at snapshot T+1 using the rainstorm sequence from its generation to snapshot T, rather than using the rainstorm object only at snapshot T as the TITAN algorithm does.  
Thus, a comparison between the PoAIR and TITAN algorithms has shown that PoAIR performs better than TITAN in terms of _POD_, _FAR_, and _CSI_ for both basic and complicated rainstorm processes,  
Figure 12: The dynamic characteristics of a rainstorm sequence which occurred between 22:00 on July 20 and 01:00 on July 21.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Rainstorm Object**} & \multicolumn{4}{c}{**Geometrical Features**} & \multicolumn{2}{c}{**Thematic Feature**} \\ \cline{2-7}  & **Eccentricity (E)** & **Rectangularity (Rt)** & **Roundness (S/d)** & **Shape Index (SI)** & **Mean Intensity** \\ \hline
22:00, July 20 & 0.391 & 0.564 & 0.262 & 0.359 & 1.597 \\
22:30, July 20 & 0.395 & 0.556 & 0.266 & 0.356 & 1.483 \\
23:00, July 20 & 0.408 & 0.600 & 0.248 & 0.364 & 1.345 \\
23:30, July 20 & 0.406 & 0.598 & 0.277 & 0.393 & 1.476 \\
00:00, July 21 & 0.407 & 0.566 & 0.259 & 0.379 & 1.609 \\
00:30, July 21 & 0.404 & 0.554 & 0.232 & 0.378 & 1.432 \\
01:00, July 21 & 0.423 & 0.553 & 0.250 & 0.385 & 1.642 \\ Mean & 0.405 & 0.570 & 0.256 & 0.373 & 1.512 \\ Standard deviation & 0.010 & 0.019 & 0.013 & 0.013 & 0.100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The geometrical and thematic features of the rainstorm sequence shown in Figure 12.  
and that PoAIR has an obvious advantage over TITAN when dealing with splitting, merging, and merging-splitting rainstorm objects. Additionally, a rainstorm outbreak on 18-21 July 2016 spanning a wide swath from Southern to Northeastern China was utilized to demonstrate the development and application of the PoAIR algorithm. PoAIR identified all the rainstorm sequences and rainstorm objects, as well as the dynamic behaviors among successive rainstorm objects, i.e., developing, merging, splitting, and splitting-merging.  
This paper adopts the widely used area-overlapping threshold and maximum search radius to track rainstorm processes. To assess the robustness of PoAIR, its performance must be further evaluated with more rainstorm processes and experiments. Additionally, PoAIR has difficulty dealing with objects whose movement direction changes dramatically. For this, prior knowledge, e.g., of atmospheric wind fields or atmospheric circulation, will be used to calculate the spatial location at snapshot T+1, instead of using the extrapolating approach based on sequences.  
Conceptualization, C.X.; data curation, C.W.; funding acquisition, C.X.; methodology, C.X. and J.L.; software, G.Y. and C.W.; validation, J.L. and G.Y.; writing--original draft, C.X.; writing--review and editing, C.X.  
This research was supported in part by the National Key Research and Development Program of China under Grant 2017 YFB0503605 and Grant 2016 YFA0600304; in part by the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant XDA19060103; and in part by the National Natural Science Foundation of China under Grant 41671401.  
We thank the data working group of the Global Precipitation Missions for providing the research data, i.e., GPM/IMERG final precipitation products, version 05B.  
The authors declare no conflict of interest.