# A Process-Oriented Method for Tracking Rainstorms with a Time-Series of Raster Datasets
## 4 Results and Discussion
### Comparisons of PoAIR and TITAN
To assess the overall performance of PoAIR compared with the original TITAN algorithm, the probability of detection (_POD_), false alarm ratio (_FAR_), and critical success index (_CSI_) scores, which are well-known indicators in precipitation evaluations [5; 27], are computed by following Equation (11). Table 3 shows their comparisons in a view of rainstorm processes, and Table 4 shows their comparisons in a view of rainstorm objects. In Table 3, if a rainstorm is identified as two or more rainstorm processes, the rainstorm processes, including the more objects, are used to calculate the evaluation indicators. \[\left\{ \begin{array}{c}POD=\frac{n_{\mathit{hist}}}{n_{\mathit{hist}}+n_{ \mathit{states}}}\\ FAR=\frac{n_{\mathit{hist}}+n_{\mathit{false}}}{n_{\mathit{hist}}}\\ CSI=\frac{n_{\mathit{hist}}}{n_{\mathit{hist}}+n_{\mathit{states}}+n_{ \mathit{false}}}\end{array} \right. \tag{11}\]  
where \(n_{\mathit{hist}}\) occurs when both the algorithm and the human observation capture a rainstorm object; \(n_{\mathit{misses}}\) occurs when human observation identifies a rainstorm object that the algorithm does not; and \(n_{\mathit{false}}\) occurs when the algorithm considers a false rainstorm object. For tracking rainstorm processes, PoAIR performs better than TITAN in terms of _POD_, _FAR_, and _CSI_ for both basic and complicated rainstorms. Regarding basic rainstorms, both PoAIR and TITAN identified Rainstorm 9 and Rainstorm 1 as two independent processes, i.e., they had the same \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Rainstorm 1**} & \multicolumn{2}{c}{**Rainstorm 2**} & \multicolumn{2}{c}{**Rainstorm 3**} & \multicolumn{2}{c}{**Rainstorm 4**} & \multicolumn{2}{c}{**Rainstorm 5**} \\ \cline{2-13}  & **PoAIR** & **TITAN** & **PoAIR** & **TITAN** & **PoAIR** & **TITAN** & **PoAIR** & **TITAN** & **PoAIR** & **TITAN** \\ \hline _POD_ (\%) & 60.00 & 60.00 & 90.91 & 54.54 & 94.70 & 78.95 & 100.00 & 58.82 & 100.00 & 72.72 \\ _FAR_ (\%) & 0.00 & 0.00 & 0.00 & 7.69 & 0.00 & 6.25 & 0.00 & 16.67 & 0.00 & 11.11 \\ _CSI_ (\%) & 60.00 & 60.00 & 90.91 & 52.17 & 94.70 & 75.00 & 100.00 & 52.63 & 100.00 & 66.67 \\ \hline  & **Rainstorm 6** & **Rainstorm 7** & **Rainstorm 8** & **Rainstorm 9** & **Rainstorm 10** \\ \cline{2-13}  & **PoAIR** & **TITAN** & **PoAIR** & **TITAN** & **PoAIR** & **TITAN** & **PoAIR** & **TITAN** & **PoAIR** & **TITAN** \\ \hline _POD_ (\%) & 90.91 & 45.00 & 100.00 & 57.89 & 100.00 & 48.00 & 62.50 & 62.50 & 100.00 & 66.67 \\ _FAR_ (\%) & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 14.28 \\ _CSI_ (\%) & 90.91 & 45.00 & 100.00 & 57.89 & 100.00 & 48.00 & 62.50 & 62.50 & 100.00 & 60.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A comparison of the rainstorm processes identified by the process-oriented algorithm for identifying and tracking rainstorms (PoAIR) and the Thunderstorm Identification, Tracking, Analysis and Nowcasting (TITAN) algorithm. detection abilities. For Rainstorm 5, TITAN identified it as two independent processes, while PoAIR identified it as one. Regarding complicated rainstorms, PoAIR combines spatial similarities and the evolution of rainstorm sequences to track rainstorm processes, which allows it to track fast-moving rainstorms. Thus, PoAIR has a higher _POD_, a lower _FAR_, and a higher _CSI_ than TITAN. In statuses of rainstorm production, development, and termination, both PoAIR and TITAN have high values of _POD_ and a high value of _CSI_, with PoAIR having slightly higher values than TITAN. As it uses evolution sequences of rainstorms to track rainstorm processes, PoAIR mistakes an independent rainstorm object at the production or termination stage as noise, e.g., the termination stage of Rainstorm 2 has a _FAR_ value of 20.00% and the production stage of Rainstorm 3 has a _FAR_ value of 26.32%.