*3.4. Potential Applications*

Based on findings of Sections 3.1–3.3, we then infer that applying IMERG6 to the study of summer convective afternoon rainfall (CAR) events over Taiwan (e.g., Figure 7a), which generally includes a diurnal precipitation maximum in the afternoon after the local thermal heating maximum (e.g., Figure 7b), can obtain results more similar to those seen in the CWB data. This inference will be clarified by the examinations presented in this sub-section. Hereafter, the methods used for the identification of CAR events follow Huang et al. [30], and are briefly summarized as follows: (1) a rainy day is defined as a day with an accumulated precipitation of ≥ 0.1 mm; (2) the accumulated precipitation of a rainy day during the time period 1200–2200 h is > 80% of the daily precipitation; (3) the accumulated precipitation of a rainy day during the time period 0100–1100 h is < 10% of the daily precipitation; and (4) days affected by other weather systems (e.g., typhoons and frontal systems) are excluded.

**Figure 7.** (**a**) Infrared cloud image, obtained from Gridded Satellite B1 Observations (https://www. ncdc.noaa.gov/gridsat/), for an example of convective afternoon rainfall (CAR) event that occurred on 20 June 2016, 17 h (local time) in Taiwan. (**b**) Time series of hourly precipitation (bars) and surface temperature (Ts, red line) averaged from local stations in Taiwan for the event shown in (**a**).

Figure 8 shows the spatial distribution for the contribution of CAR activities (including occurrence frequency and precipitation amount) to the total summer precipitation events, averaged over the summers of 2014–2017. In Figure 8a (Figure 8b), the CWB data shows that CAR events contribute more than 40% (30%) of the occurrence frequency (precipitation amount) of the total precipitation events in most areas of Taiwan. Furthermore, similar to Figure 5a, two maximum centers are revealed in the CAR activities observed by the CWB data. Despite the location difference, all SPPs are able to show two maximum centers in the CAR activities; however, GSMaP7 apparently underestimates the most in the contribution of CAR activities to the total precipitation events.

**Figure 8.** Contribution of CAR events to total precipitation events during the summers of 2014–2017: (**a**) frequency of occurrence, (**b**) amount of precipitation. In (**a**,**b**), contribution <sup>=</sup> CAR events Total precipitation events <sup>×</sup> 100%.

By comparing the CWB data with the four SPPs in Figure 8, two statistical scores (Scorr and RMSE) were calculated and documented in Table 3. It can be noted in Table 3 that IMERG6 (with the highest Scorr and the lowest RMSE) outperforms the other SPPs, while the greatest bias is seen in GSMaP7. This is also consistent with what revealed in Table 2, suggesting that the higher (lower) performance of IMERG6 (GSMaP7) in depicting CAR activities may be attributed to its higher (lower) performance in illustrating the diurnal variation of precipitation over Taiwan.

**Table 3.** Statistical values for the comparison between the CWB data and SPPs shown in Figure 8. The unit of RMSE is %. The lowest value of Scorr and the highest value of RMSE are marked by \*. The sample size is 392 grid points.


Additionally, we examine the performance of TRMM7 and IMERG6 in depicting the interannual variation of CAR activities area-averaged over Taiwan during the summers of 2000–2017. Figure 9a shows that both TRMM7 and IMERG6 are capable of depicting the interannual variation of contribution of CAR events to the total precipitation amount, similar to those seen in the CWB data. The Tcorr between the time series of TRMM7 (IMERG6) and the CWB data in Figure 9a is approximately 0.73 (0.80), which passes the 99% significant test.

**Figure 9.** (**a**) The contribution of CAR events to the total precipitation amount that fell during the summers of 2000–2017. (**b**) The contribution of CAR days to total rainy days. (**c**) The contribution of CAR intensity (i.e., mean rain rate; unit: mm per event) to the intensity of total rainy events. In (**a**–**c**), contribution <sup>=</sup> CAR events Total precipitation events <sup>×</sup> 100%. In (**d**), the comparison of Tcorr and RMSE between time series in (**a**–**c**) is given, using the CWB data as the reference base, and denoted a-b-c, respectively. In (**d**), the purple color and blue color represents results related to TRMM7 and IMERG6, respectively.

By separating the precipitation amount into the occurrence frequency and the intensity (i.e., precipitation amount = occurrence frequency × intensity), it is however noted from Figure 9b that both IMERG6 and TRMM7 tend to underestimate the contribution of CAR events to the occurrence frequency of total precipitation events, as compared to the CWB data. This might be because the SPPs, which utilize precipitation estimation from infrared and passive microwave sensors, are poor at retrieving local precipitation events over complex mountainous areas [42,48].

In contrast to Figure 9b, it can be noted in Figure 9c that both IMERG6 and TRMM7 tend to overestimate the contribution of CAR events to the intensity of total precipitation events. This might be because that satellite methods assume heavy precipitation results from deep clouds [42], and CAR events in Taiwan are belong to local deep convections [30]. Despite the bias seen in Figure 9b,c, we note from Figure 9d that IMERG6 (with higher Tcorr and lower RMSE) outperforms TRMM7 overall in illustrating the interannual variations of CAR activities. Therefore, we suggest that using IMERG6 to replace TRMM7 can benefit the researcher by obtaining more accurate characteristics of CAR in Taiwan.
