## 4 Discussion
The faster spin-up for hydrometeors in POD covers the shortage of traditional variational assimilation method, which is beneficial to short-term forecast, especially less than 6 h. Besides, since POD-4 DEnVar method adopts the variational framework, the dynamic constraint and existing capability of variational assimilation can be added in POD-4 DEnVar [43; 44; 45]. In all, POD-4 DEnVar combines the advantages of traditional variational assimilation method and EnKF. Table 2 summarizes the difference of variational data assimilation, EnKF and POD-4 DEnVar method based on literature [37; 43; 44; 45] and this paper. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Advantages**} & \multicolumn{2}{c}{**Variational Data Assimilation**} & \multirow{2}{*}{**EnKF**} & \multirow{2}{*}{**POD-4 DEnVar**} \\  & **(3/4 DVAR)** & & \\ \hline Benefit from the use of Flow-dependent \(\mathbf{B}\) & & \(\surd\) & \(\surd\) \\ \hline Better localization for satellite and radar & & & \(\surd\) \\ observations [44] & \(\surd\) & & \(\surd\) \\ Easiness to add the dynamic constraint of & \(\surd\) & & \(\surd\) \\ \hline No need for linearized models [37] & & \(\surd\) & \(\surd\) \\ \hline More use of existing capability in VAR & \(\surd\) & & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of variational data assimilation, ensemble Kalman filter (EnKF), and POD-4 DEnVar methods. Using POD-4 DEnVar assimilation method, previous researchers studied the impact of assimilating satellite and radar data individually [24; 26]. In this study, simultaneous satellite and radar data assimilation is performed. Compared to individual assimilation, assimilating two kinds of data provide both information on satellite and radar at the same time. Satellite radiance represents the most numerous and widely available observations and provide information where other observations are not present [27]. Radar observations have much higher-resolution, which is useful for local convective weather system [47]. Increment results show that assimilating both observations can change the large-scale and local initial field. Temperature and moisture are changed almost all over the study domain, while wind field and hydrometeor are only changed in the precipitation area. Similar to results from [PERSON] et al. [48], who assimilated two kinds of observations results in the reasonable analysis of wind, temperature and moisture field, improving the rainstorm forecast. In addition, [PERSON] et al. [24] found that the benefits of satellite assimilation show in temperature, moisture and wind field, without rainwater included in the control variables. In this study, due to the relationship between radar reflectivity and rainwater Equation (14), rainwater in included in the control variables when assimilating both satellite and radar data. Thus, the increase of initial rainwater help improves rainfall forecast especially, compared to assimilating satellite radiance only. When only assimilating radar data with POD-4 DEnVar method, [PERSON] et al. [26] found that initial rainwater is also changed. However, with the satellite data included in this study, the change on initial hydrometeor (Figure 14) is obviously more widely distributed than [PERSON] et al. (Figure 10 in [26]). Therefore, assimilating both satellite and radar data with POD-4 DEnVar method provides better initial analysis and forecast for heavy rainfall. Furthermore, previous assimilation with POD-4 DEnVar method focus on just one case study. [PERSON] et al. [24] investigated the impacts of POD-4 DEnVar assimilating satellite radiance on a heavy rainfall on 29 June 2009. [PERSON] et al. [26] assimilated radar data with POD-4 DEnVar method for heavy rainfall on 08 July 2010. In this study, instead of single analysis and forecast for one case, the performance of the POD-4 DEnVar assimilation system is evaluated through two 11-day continuous analysis and forecast in 2016 and 2017, during which obvious summer precipitation took place.