## 4 Discussion  
It can be inferred from Figure 5 that the threshold method used in the traditional SI method could not provide reasonable rain area delineation over the study area. According to the research of [PERSON] [20] and [PERSON] [21], SI is a measurement of the microwave scattering intensity at the high frequencies of the clouds, which is highly related to the distribution of the hydrometeors in clouds. In theory, the high values of SI usually mean a large size and density of water-drops and ice crystals in the clouds, which lead to a high probability of rainfall. However, in fact, the occurrence of precipitation on land not only depends on the physical properties of hydrometeors in the clouds, but also depends on many other factors, such as complicated underlying surface and other meteorological conditions [22]. The disturbance of these factors complicates the relationship between the occurrence of precipitation and the values of SI. Therefore, the rain areas cannot be delineated accurately using the threshold method used in the traditional SI method. In contrast, the PNN rain delineation method does not rely on a given threshold, but depends on a Bayes-optimal statistical network. During the feedback learning process of PNN, the microwave characteristics of the no rain pixels with high scattering effect and the rain pixels with low scattering effect in the training dataset could be extracted, which should be helpful to identify the raining/no rain pixels with similar microwave characteristics of other areas. This assumption can be demonstrated by the results shown in Figure 5, which suggests that the PNN method can not only substantially reduce the amount of false alarm precipitation and misdetected precipitation in comparison with the SI method, but also show a much better performance than other widely used classification methods (TRMM 2A12 and Kmean). In general, the PNN model is a promising method to provide accurate rain area delineation based on satellite observations. The accuracy of the rain and no rain areas in TRMM 2A12 over the Yangtze River Basin was also validated. As well-acknowledged and widely-used satellite precipitation datasets, the rainfall information in TRMM 2A12 was derived from TMI observations using a physical retrieval algorithm termed the Goddard profiling algorithm (GPROF) models. This model first built a dataset containing the potential hydrometeor profiles and their computed TBs at microwave channels. Then, the rainfall information was obtained over a location by matching the TBs of this location with the corresponding potential hydrometeor profiles using the Bayesian approach. However, [PERSON] [18] pointed out a potential drawback of the GPROF algorithm, which was that this algorithm was optimized for particular regions and datasets, leading to regional and seasonal errors contained in the TRMM 2A12. As shown in Figure 5, the TRMM 2A12 could only detect 57% rain pixels of the 16 rain events, while misclassifying 33% of no rain pixels as under raining conditions, and the ETS, HK, CSI and HSS of TRMM 2A12 were lower than the SI_ threshold method and the PNN method. These results indicate that the TRMM 2A12 could not provide reasonable rain area delineation over the Yangtze River Basin. The misclassified precipitation will be inherited and exacerbated when this dataset is used to drive hydrological models and merged with other precipitation datasets [14,15]. Unfortunately, the false alarm and misdetected precipitation in TRMM 2A12 has rarely been noted and discussed so far. The PNN-based rain area delineation method proposed by this study provides a promising way to improve the performance of TRMM 2A12 over the regions with some rain gauge observations. Figure 7 indicates that using the PNN method, the performance of the SI method can be improved in most cases. This reveals the importance of rain area delineation in the retrieval of high-resolution satellite precipitation.