## 3 Results
### Validation and Comparison of the PNN Method  
First, the performance of the PNN method was compared with the threshold method used in the conventional SI method (SI_threshold). Unlike the constant threshold (SI \(>\) 10) proposed by [PERSON] [20], the threshold of each case was selected by maximizing the value of HSS according to the research of [PERSON] _et al._[36]. In this study, the thresholds of SI ranged from 4.7 to 6.4 with a mean value of 5.1. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rain Event** & **Time** & **Number of Rain Pixels** & **Number of No Rain Pixels** \\ \hline
1 & June 25 at 2 am & 419 & 2757 \\
2 & June 27 at 8 pm & 549 & 2607 \\
3 & June 28 at 7 pm & 879 & 2831 \\
4 & June 29 at 0 am & 1048 & 2286 \\
5 & July 2 at 5 pm & 242 & 3470 \\
6 & July 6 at 3 pm & 128 & 3518 \\
7 & July 23 at 7 pm & 453 & 2807 \\
8 & July 26 at 4 am & 482 & 2215 \\
9 & July 26 at 9 am & 926 & 2662 \\
10 & July 27 at 8 am & 483 & 2525 \\
11 & July 29 at 3 am & 330 & 3268 \\
12 & July 31 at 3 am & 483 & 2470 \\
13 & August 4 at 1 am & 446 & 2646 \\
14 & August 5 at 0 am & 317 & 2935 \\
15 & August 5 at 5 am & 295 & 3349 \\
16 & August 9 at 9 pm & 621 & 2297 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The information of the 16 rain events over the Yangtze River Basin through June, July and August 2009. Figure 3: The rain areas observed by (**a**) rain gauge and delineated by (**b**) dynamic cluster K-means (Kmean); (**c**) TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product (TRMM 2A12); (**d**) threshold method of Scatter Index (SI_threshold); and (**e**) probabilistic neural network (PNN) methods. (On 26 July 2009, at 9 am). Figure 4: The misdetected and false alarm precipitation of the rain area delineated by **(a)** dynamic cluster K-means (Kmean); **(b)** TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product (TRMM 2A12); **(c)** threshold method of Scatter Index (SI_threshold); and **(d)** probabilistic neural network (PNN) methods (On 26 July 2009, at 9 am). As shown in Figure 5, the FAR of SI_threshold ranged from 0.21-0.58 with a mean value of 0.39, whereas the FAR of PNN decreased to 0.12-0.38 with a mean value of 0.23 (Figure 5a), indicating that the PNN contained less false alarm precipitation than SI_threshold. The POD of PNN does not show an obvious increase compared with SI_threshold (Figure 5b), indicating that the amount of misdetected precipitation in PNN is similar or less than SI_threshold in most cases. The mean values of ETS (Figure 5c), CSI (Figure 5d), HK (Figure 5e) and HSS (Figure 5f) of SI_threshold were 0.40, 0.46, 0.59 and 0.56, respectively, whereas the mean values of these categorical statistics of PNN increased to 0.52, 0.57, 0.65 and 0.68. It can be inferred from the results that the PNN method could delineate the rain  
Figure 5: The results of **(a)** false alarm ratio (FAR), **(b)** probability of detection (POD), **(c)** equitable threat score (ETS), **(d)** critical success index (CSI); **(e)** Hanssen and Kuiper discriminant (HK) and **(f)** Heidke skill score (HSS) of the rain area delineated by the threshold method of Scatter Index (SI_threshold), probabilistic neural network (PNN), TRMM Microwave Imager (TMI) Level 2 Hydrometeor Profile Product (TRMM 2A12) and dynamic cluster K-means (Kmean) methods. areas more accurately compared with the threshold method used in the traditional SI method. To further validate the performance of the PNN rain delineation method, the rain area delineated by TRMM 2A12 and the results of Kmean were compared. In this study, the surface rainfall stored in TRMM 2A12 was extracted by the \"hdftool\" of the MATLAB 2009a software and resampled to 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) using the nearest neighbor method provided by the ArcGIS software. Then, the rain pixels were selected by the surface rain above 0 mm/h.