*4.1. Storm Analysis*

We identified 120 storm events observed and estimated by GPM-IMERG at an hourly scale for the 2014–2017 monsoon seasons. Figure 5 shows the scatter plot of the main storm characteristics (total volume, duration, spatial extent, and maximum intensity) and classification between shortand long-lived storm events using the k-means cluster analysis. For all events, 68 storms (56%) were classified as short-lived storms while 52 (44%) of storms were classified as long-lived events. Short-lived events had a duration that ranged between three and 17 h, with a maximum spatial extent of 42 thousand km2. Long-lived events had a duration ranging between 18 and 31 h and covered between 54 and 110 thousand km2. In terms of total volume and maximum intensity, short-lived events have a total volume of up to 0.15 km3 with low and intense storms ranging from 3 to 82 mm/h. On the other hand, long-lived storms are comprised of medium and high-intensity events with a total volume ranging from 0.27 to 0.65 km3. Table 1 describes the observed storm characteristics for short and long-lived event types.

**Figure 5.** Short- and long-lived cluster analysis classification for observed events during monsoon season 2014–2017.


**Table 1.** Storm characteristics for short- and long-lived event types.

*4.2. Results for Bias Correction*

We selected a short-lived and a long-lived storm in order to describe the workflow for displacement and volume correction made by ST-CORAbico. Figures 6 and 7 present the bias correction steps for each storm event type. Panel (a) shows the spatial distribution of the observed and satellite events as well as the bias-corrected satellite storm events that were obtained from the correction of location and magnitude errors in ST-CORAbico. Panel (b) describes the displacement and volume corrections. Panel (c) presents the four-dimensional (4D) spatiotemporal evolution of the observed and satellite as well as the bias-corrected storm (time in the z-axis). Panel (d) shows the bias and RMSE statistics as well the scatter and correlation between the observed storm and original and bias-corrected satellite events.

Both examples (Figures 6 and 7) show the importance of bias correction. In both the short and long-lived event scenarios, GPM-IMERG had a longer duration with a larger footprint. However, the long-lived event presented a better spatial agreement than the short-lived event. In terms of magnitude, GPM-IMERG considerably overestimated the total volume and rainfall intensity of the storm. Overall, the performance of GPM-IMERG shows a positive bias and high RMSE, mostly being caused by an excess of rainfall. The correlation coefficients for short- and long-lived event scenarios were 0.7 and 0.5, respectively.

**Figure 6.** Performance of ST-CORAbico for a short-lived storm event (2014-08-27). (**a**) total events for observed, satellite and ST-CORAbico; (**b**) volume, displacement correction maps; (**c**) four-dimensional (4D) spatiotemporal evolution (lat, lon, time, intensity); and, (**d**) bias, RMSE statistics, and scatter and correlation between observed and estimated rainfall values.

The corrections in displacement and volume made by ST-CORAbico displayed notable changes in the satellite storm structure. In both scenarios, RMSE and bias were mostly reduced by correction due to volume, contributing 40 to 60% of the RMSE reduction and around 70% of the total bias reduction for both events. Displacement correction had an important impact on the reorientation of the satellite storm. The individual correction contributed to 5% of the RMSE correction and 10% reduction of the total bias for the short-lived event. In the case of the long-lived scenario, displacement correction contributed almost 15% of the RMSE reduction and 20% of the total bias reduction. In terms of the correlation coefficient, displacement and volume correction that were made by ST-CORAbico showed a marginal impact on the spatial correlation for the short-lived events. For the long-lived scenario, this did not impact the spatial correlation.

**Figure 7.** Performance of ST-CORAbico for a long-lived storm event (2014-07-21). (**a**) total events for observed, satellite and ST-CORAbico; (**b**) volume, displacement correction maps; (**c**) 4D spatiotemporal evolution (lat, long, time, intensity); and, (**d**) bias, RMSE statistics and scatter and correlation between observed and estimated rainfall values.

Figure 8 presents the performance of ST-CORAbico for short- and long-lived storm events. This figure describes the density distribution of RMSE (a–b), bias (c–d), and correlation coefficient (e–f) of the short- and long-lived storms estimated by GPM-IMERG, ST-CORAbico, and the individual corrections due to displacement and volume. It was found that ST-CORAbico has a smaller error distribution in RMSE and bias for short- and long-lived storm events when compared with the original GPM-IMERG. This error reduction is mostly caused by the correction due to volume. Displacement correction was an important factor in reducing the bias, especially for long-lived storm events. The results from the correlation coefficient showed that ST-CORAbico had a marginal effect on the spatial correlation of the storm event. Overall, it was found that ST-CORAbico considerably reduced the systematic error of GPM-IMERG.
