## 6 Application of Precipitation Nowcasting
### Error Analysis  
The RMSE, MAE, CC and BIAS were calculated between STEPS nowcast and GPM precipitation data at different lead times for each event, the profiles of which are shown in Figure 12a-d with their mean value lead times presented in Table 7. The average RMSE and MAE values between STEPS nowcast and GPM for five events during the whole period were obtained (7.1 mm and 4.2 mm, respectively), which indicate the errors related to the estimation of GPM precipitation. The results demonstrate that the errors obtained from the nowcast data are relatively small but increase with growing lead time, and that the errors of RMSE and MAE increase as the lead time increases. For CC and BIAS, both of them decreased with the increased lead time, and the accuracy of precipitation nowcasting decreases as the lead time grows, which concurs with finding of a previous study [55]. Overall, the trend of nowcast precipitation decreases as the lead time increases.  
In order to investigate the variability of errors, box plots were produced as presented in Figure 13a-d. Box plots of errors of the GPM IMERG product provide details of the accuracy of nowcasting. The lower and upper edges of the central box are the third (75%) and first (25%) quantiles that represent the Interquartile Range (IQR), as the central inside band and cross sign represents the median and mean values, respectively [56].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Lead Times** & **RMSE** & **MAE** & **CC** & **BIAS** \\ \hline
30 & 3.69 & 1.91 & 0.68 & 0.88 \\
60 & 4.50 & 2.20 & 0.61 & 0.85 \\
90 & 4.80 & 2.69 & 0.56 & 0.84 \\
120 & 5.93 & 3.71 & 0.50 & 0.82 \\
150 & 6.17 & 4.96 & 0.44 & 0.79 \\
180 & 5.92 & 3.90 & 0.42 & 0.78 \\
210 & 6.44 & 4.75 & 0.38 & 0.78 \\
240 & 6.57 & 4.98 & 0.35 & 0.74 \\
270 & 6.85 & 4.24 & 0.32 & 0.75 \\
300 & 7.20 & 5.47 & 0.27 & 0.76 \\
330 & 7.77 & 5.96 & 0.24 & 0.73 \\
360 & 10.31 & 5.78 & 0.20 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of evaluation metrics for GPM IMERG versus nowcasts results along the lead times.  
Figure 11: Precipitation on 22 October 2015 at 9:00, based on (**a**) GPM estimated precipitation and (**b**) 30-min forecast precipitation from STEPS precipitation nowcast.  
Figure 12: Summary of (**a**) root mean square error; (**b**) mean absolute error; (**c**) correlation coefficient; and (**d**) BIAS profiles from the five precipitation events, obtained by STEPS model at different lead times.  
Figure 13: _Cont._  
Each box plot presents four evaluation metrics. A similar pattern in the variations of RMSE and MAE, CC and BIAS with lead time was observed in the five precipitation events, although differing magnitudes are given. An asymptotic growth in MAE and an asymptotic reduction in CC with higher lead time was found. For smaller lead times for the four metrics, generally the spread box is larger. Plus, since the values of CC less than 0.5 after the lead time becomes 120 min, the quality of precipitation nowcasting by using the GPM data is high before 120 min. When the lead time reaches 120 min, the errors of nowcasts increase rapidly. So, the GPM IMERG precipitation data is satisfying for real-time short-term nowcasting, particular for two hours ahead.