## 4 Results and Discussion
### Statistical Performance of Satellite Precipitation Estimates
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Season** & **Product** & **CC** & \begin{tabular}{c} **RMSE** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **ME** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **RRMSE** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **RB** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **POD** \\ \end{tabular} & **FAR** & **FBI** & **ETS** \\ \hline Spring & IMERG-UC & 0.56 & 3.26 & \(-\)0.62 & 242.87 & \(-\)46.38 & 0.51 & 0.30 & 0.74 & 0.33 \\  & IMERG-C & 0.61 & 3.17 & \(-\)0.18 & 236.01 & \(-\)13.60 & 0.64 & 0.38 & 1.03 & 0.36 \\  & GSM-MVK & 0.38 & 6.24 & 0.43 & 464.42 & 31.94 & 0.64 & 0.44 & 1.14 & 0.31 \\ Summer & MSDA-Gauge & 0.71 & 2.71 & \(-\)0.21 & 201.69 & \(-\)15.53 & 0.82 & 0.30 & 1.17 & 0.52 \\ Summer & IMERG-UC & 0.67 & 5.15 & \(-\)1.26 & 139.24 & \(-\)34.02 & 0.72 & 0.22 & 0.93 & 0.39 \\  & IMERG-C & 0.68 & 5.27 & \(-\)0.10 & 142.52 & \(-\)2.74 & 0.80 & 0.26 & 1.07 & 0.40 \\  & GSM-MVK & 0.59 & 8.01 & 0.86 & 216.56 & 23.16 & 0.78 & 0.27 & 1.08 & 0.37 \\  & GSM-Gauge & 0.76 & 4.36 & \(-\)0.08 & 117.87 & \(-\)20.3 & 0.91 & 0.24 & 1.20 & 0.50 \\ Autumn & IMERG-UC & 0.66 & 3.18 & \(-\)0.57 & 218.98 & \(-\)39.09 & 0.60 & 0.25 & 0.81 & 0.41 \\  & IMERG-C & 0.71 & 3.12 & \(-\)0.11 & 214.77 & \(-\)7.44 & 0.69 & 0.31 & 1.01 & 0.43 \\  & GSM-MVK & 0.44 & 6.96 & 0.52 & 478.63 & 35.88 & 0.70 & 0.37 & 1.12 & 0.39 \\  & GSM-Gauge & 0.77 & 2.67 & \(-\)0.06 & 183.5 & \(-\)4.41 & 0.87 & 0.27 & 1.19 & 0.57 \\ Winter & IMERG-UC & 0.54 & 1.45 & \(-\)0.21 & 522.96 & \(-\)76.56 & 0.13 & 0.43 & 0.23 & 0.11 \\  & IMERG-C & 0.58 & 1.39 & \(-\)0.17 & 500.66 & \(-\)62.17 & 0.19 & 0.54 & 0.42 & 0.14 \\  & GSM-MVK & 0.30 & 1.95 & \(-\)0.05 & 701.76 & \(-\)18.30 & 0.29 & 0.66 & 0.85 & 0.16 \\  & GSM-Gauge & 0.58 & 1.40 & \(-\)0.13 & 501.71 & \(-\)45.07 & 0.52 & 0.38 & 0.84 & 0.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Seasonal statistics of four satellite precipitation estimates (IMERG-UC, IMERG-C, GSMap-MVK, and GSMaP-Gauge) against ground observations over the Tibetan Plateau (TP) during the study period of April 2014–March 2017. Figure 6: **(a) Average monthly precipitation time series and monthly variations of statistical indices: (b) CC, (c) RMSE, and (d) RB. **  
For analyzing the error characteristics of different precipitation event, following the error decomposition approach proposed by [PERSON] et al. [69], the total bias can be decomposed into different parts: hit bias, bias due to rainfall misses, bias due to false detections, and bias with selected threshold. As shown in Figure 7, hit bias and total bias share considerable similarities in their spatial distributions, suggesting that hit bias is the dominant component of total bias. Considering that the negative bias with missed precipitation and positive bias with false precipitation have opposite signs, they can offset each other, resulting in a smaller total bias of satellite precipitation. The lighter precipitation (<1 mm/day) that we considered unreliable for either gauge data or satellite measurements contributed to only a small part of total bias and can be ignored (Table 4). In addition, the error components also showed seasonal dependence. Generally speaking, it is apparent that the values of total bias are lower in the summer than in the winter. Particularly, in the winter, higher miss bias was found, and missed precipitation was the dominant source of errors. This phenomenon indicates that the satellite estimates miss a lot of precipitation events in winter, and confirms our aforementioned speculation: the GPM-era satellite precipitation products still exhibit some deficiencies for detecting snowing events. Figure 7: Spatial patterns of the error components of satellite precipitation estimates against CGDPA at the 132 grid central points (mm/day): total bias (**first row**), hit bias (**second row**), missed precipitation (**third row**), false precipitation (**fourth row**), and bias with selected threshold (**fifth row**).