**4. Results**

#### *4.1. Spatial Distributions of Precipitation Estimates from FY-2E, FY-2G, and IMERG*

The spatial distributions of FY-2E QPE, FY-2G QPE, and IMERG data in the summer of 2018 over mainland China are shown in Figure 2b–d, respectively, while Figure 2a displays the spatial distribution of precipitation obtained by inverse distance weighted (IDW) interpolation based on ground observations. All three satellite-based precipitation products present a distinct decreasing spatial variation of precipitation from the southeast to the northwest, which is consistent with that presented by ground observations. The spatial patterns of FY-2G QPE and IMERG are consistent with the patterns of interpolated results based on rain gauge data. However, both FY-2E and FY-2G products show an absence of data over the Tibetan Plateau and Qaidam Basin in northwest China. Moreover, both products do not provide precipitation estimates over northern parts of Heilongjiang Province, China, which exceed the extent of 50◦N. Conversely, IMERG products provide full coverage precipitation estimates over mainland China.

**Figure 2.** Spatial patterns of precipitation products estimated by (**a**) interpolated results based on rain gauge data, (**b**) Fengyun (FY)-2E quantitative precipitation estimates (QPE), (**c**) FY-2G QPE, and (**d**) Integrated Multi-satellitE Retrievals for GPM (IMERG) over mainland China in summer, 2018.

#### *4.2. Validations of the Three Precipitation Products in the Summer, 2018*

To evaluate the performances of FY-2E, FY-2G, and IMERG products, the three satellite-based precipitation products were validated separately against rain gauge data. Figure 3a–c show the validation results of FY-2E, FY-2G, and IMERG against ground observations in June (first row), July (second row), and August (third row) 2018, respectively. In general, according to the validation results, FY-2G QPE and IMERG outperform FY-2E QPE at monthly scale, with a CC of 0.65, 0.87, and 0.90 (0.90, 0.80, and 0.82) and bias of −8.13%, −3.97%, and −6.36% (8.40%, 7.84%, and 2.77%), in June, July, and August, respectively. In terms of RMSE and MAE, the results of FY-2G QPE are also lower than those of FY-2E QPE and IMERG for the entire summer of 2018, except for the worse performance compared with IMERG in June. In addition, IMERG shows small degrees of overestimation (bias of less than 10%). On the contrary, FY-2E QPE shows significant overestimation compared with ground observations, with bias of more than 30% in June and July, while FY-2G QPE also underestimated precipitation, but to a lesser degree (bias of more than −10%), for the entire summer of 2018.

**Figure 3.** Validations of (**a**) FY-2E QPE, (**b**) FY-2G QPE, and (**c**) IMERG data against ground observations at monthly scale over mainland China in summer, 2018.

Figure 4a–d show Taylor diagrams of the performances of FY-2E QPE, FY-2G QPE, and IMERG against gauge precipitation measurements, in summer, June, July, and August, respectively. Taylor diagrams provide a graphical way to comprehensively evaluate the similarities between sets of patterns and observations [32]. Three classical indicators, namely, the CC, centered root-mean-square di fference (CRMSD), and standard deviation (STD), are presented in a single 2D diagram, which reflect how closely the various patterns in satellite-based precipitation products match those in ground observations. If the estimated pattern is closer to the observations than other patterns in the diagram, then it means that the accuracy of the estimates is better than those of others. Taylor diagrams can convey more information more clearly than an ordinary table. They are useful because the strengths and weaknesses of the three statistical indexes are shown in the same diagram, and are thus less ambiguous [33,34].

We can conclude from the Taylor diagrams that the precipitation patterns of FY-2G QPE are the most similar to those of ground observations, since FY-2G QPE exhibits the best performances, with an RMSD value of around 48.63 mm and CC value of around 0.87 in July (Figure 4c), and an RMSD value of around 48.94 mm and CC value of around 0.90 in August (Figure 4d). In June, IMERG has the best similarity to ground observations, with RMSD and CC values of around 48.12 mm and 0.89, respectively (Figure 4b). Meanwhile, FY-2E QPE displays the largest values of RMSD, meaning that it has the lowest similarity to ground observations in all the four periods.

**Figure 4.** Taylor diagrams of performances of FY-2E QPE, FY-2G QPE, and IMERG against ground observations in terms of the centered root-mean-square difference, correlation coe fficient, and standard deviation in (**a**) summer, (**b**) June, (**c**) July, and (**d**) August, 2018.

#### *4.3. Validations of the Three Precipitation Products Based on Statistical Indices at Hourly Scale*

Figure 5a–d illustrate the spatial patterns of CC, RMSE, bias, and MAE of FY-2E QPE (first column), FY-2G QPE (second column), and IMERG (third column), respectively, against ground observations at hourly scale, over mainland China in summer, 2018. It is obvious that FY-2G QPE outperforms FY-2E QPE and IMERG, with the best spatial patterns and numerical ranges of all the four indices, while IMERG performs better than FY-2E QPE. The CC of FY-2E QPE in mainland China is generally lower than 0.3, while the CC values of IMERG vary from 0 to 0.5, and are rarely larger than 0.5. Among the IMERG data, the best performing CC values are mainly distributed in the middle part of mainland China. As for FY-2G QPE, the CC values are larger than 0.6 over more than half of the area of China, especially in the eastern and central parts of mainland China. All three satellite-based precipitation products perform poorly in the southern and northwestern provinces of China. In terms of bias, FY-2G QPE also has the best performance, with the lowest bias over the majority of mainland China. The bias values of IMERG are greater than 10% over half of mainland China, especially in northwestern China, where the bias values are generally more than 50%.

**Figure 5.** Spatial patterns of performances for FY-2E QPE, FY-2G QPE, and IMERG in terms of (**a**) the correlation coefficient (CC), (**b**) root mean square error (RMSE), (**c**) bias, and (**d**) mean absolute error (MAE) against ground observations at hourly scale, respectively.

Averaged values of the four statistical indices of the three products at hourly scale in June, July, August, and summer are displayed in Table 3. FY-2G QPE has the largest values of CC of 0.45, 0.66, 0.66, and 0.59 in June, July, August, and summer, respectively. The averaged RMSE and MAE values of all the three products are nearly smaller than 1.80 and 0.40 mm, respectively. IMERG shows overestimation in June (14.59%), July (11.34%), and August (10.07%), while FY-2G QPE underestimates the precipitation in all three months (−7.45% in June, −2.28% in July, and −4.34% in August, respectively). The averaged bias values of FY-2E QPE show significant variation. FY-2E QPE greatly overestimates precipitation in June (35.35%) and July (36.07%), while underestimates precipitation in August (−25.42%).


**Table 3.** Averaged statistical indices for FY-2E QPE, FY-2G QPE, and IMERG at hourly scale over mainland China in summer, 2018.

Figure 6 displays the temporal patterns of performances at hourly scale for the three types of products compared to ground measurements. The statistical indices were calculated by the following steps: firstly, the gauge-based data and satellite-based data were extracted for 24 h; secondly, the statistical indices were calculated for each hour; and finally, the results from all stations across the country were averaged. Generally, both the performances of FY-based and GPM-based precipitation products are poorer during the period from 06:00 to 10:00 Coordinated Universal Time (UTC) than other periods in one day. Specifically, in Figure 6a, CC reaches its highest value during the periods of 00:00–3:00 and 18:00–24:00, and obtains its lowest value at about 09:00 (meant 9:00–10:00 UTC, which is the same as below), during the entire day. At about 15:00–17:00, the CC values of IMERG exhibit a decreasing trend, which does not appear in either FY-2E or FY-2G products. The variation of RMSE is contrary to that of CC, which means that a higher CC value always indicates a lower RMSE value (Figure 6b). It is clear that the RMSE of IMERG at 03:00 is the lowest in the 24-h period, at which time the curves of RMSE for FY-2E QPE and FY-2G QPE are smoother. As for the variations of bias (Figure 6c), FY-2E QPE and IMERG show overestimates (i.e., bias greater than 0%) in most of the periods, while FY-2G QPE generally underestimates precipitation for the entire day. Regarding the variations in MAE (Figure 6d), all three precipitation products show similar trends.

**Figure 6.** *Cont*.

**Figure 6.** Temporal patterns of performances of FY-2E QPE, FY-2G QPE, and IMERG in terms of (**a**) the CC, (**b**) RMSE, (**c**) bias, and (**d**) MAE against ground observations, respectively.

#### *4.4. Contingency Indices of the Three Precipitation Products at Hourly and Daily Scales*

Figure 7a–d display the spatial distributions of the contingency indices (POD, FAR, CSI, and FBI, respectively), generated by IDW interpolation based on validation results of corresponding rain gauge data, over mainland China during summer, 2018. Generally, the POD values of FY-2G QPE (>0.70) are much better than those of FY-2E QPE and IMERG, across mainland China. The POD values of IMERG are around 0.4 to 0.7 over most areas, while the POD values of FY-2E QPE are the smallest in most parts of mainland China (<0.5), especially in the northwest (<0.3) (Figure 7a). The FAR values of FY-2E QPE are above 0.5 over most regions and are larger than 0.8 in northwestern China, which is similar to the case of the IMERG products. As for FY-2G QPE, the FAR values (<0.6) are smaller than both values of FY-2E QPE and IMERG. Regarding the distributions of CSI (Figure 7c), FY-2G QPE shows a better performance than FY-2E QPE and IMERG, with values of around 0.4 to 0.7 over mainland China. The CSI values of FY-2E QPE and IMERG show similar spatial distributions. Both CSI values of FY-2E QPE and IMERG are lower than 0.4 overall, and lower than 0.2 in northwestern China. The FBI values of IMERG are higher than 1.8 in more than half of the areas, which indicates high overestimates in precipitation over such regions. The FBI values of FY-2G QPE are also greater than 1.2 over most parts of mainland China, reflecting overestimates in these areas, but to a lower degree compared with estimates of IMERG data. FY-2E QPE tends to overestimate precipitation in south, northeast, and northwest China, with FBI values larger than 1.6, and underestimate precipitation in the west and east coast regions of China, with values smaller than 1.

**Figure 7.** Spatial patterns of the performance of FY-2E QPE, FY-2G QPE, and IMERG in terms of the (**a**) probability of detection (POD), (**b**) (false alarm ratio) FAR, (**c**) critical success index (CSI), and (**d**) frequency bias index (FBI) compared to ground observations at hourly scale, respectively.

Averaged values of contingency indicators of the three products at hourly scale in June, July, August, and summer are exhibited in Table 4. FY-2G QPE shows the best values of POD in all the four periods compared with the other two products (around 0.61 in June, 0.84 in July, 0.84 in August, and 0.77 in summer). FY-2E QPE and IMERG have higher averaged values of FAR than those of FY-2G QPE, which are relevant to the lower values of CSI of both FY-2E QPE and IMERG. The values of CSI of FY-2E QPE are the lowest in each month, as well as for the entire summer. The averaged FBI values of all three precipitation products are much greater than one, which indicates that each of the three products show a larger proportion of false alarms than false negatives.


**Table 4.** Averaged contingency indices for the FY-2E QPE, FY-2G QPE, and IMERG at hourly scale over mainland China in summer, 2018.

Figure 8a displays the temporal variations of POD of FY-2E QPE, FY-2G QPE, and IMERG. The values of POD of FY-2G QPE are the largest during the entire day, with values ranging from 0.75 to 0.80. The values of POD of IMERG are smaller than those of FY-2G QPE at each hour, with values varying from 0.55 to 0.70. The temporal variations of POD of IMERG are not smooth. IMERG shows a peak around 09:00 and valleys at 02:00, 13:00, and 17:00. FY-2E QPE shows the smallest values of POD (<0.57) compared with those of FY-2G QPE and IMERG, which suggests that FY-2E QPE could not detect rainfall events reasonably and effectively during the summer. Figure 8b shows the temporal variations of FAR. In general, FY-2G QPE shows the lowest FAR values at each time during the entire day, while the FAR values of IMERG are smaller than those of FY-2E QPE overall. As for the performances of FY-2G QPE, the FAR values exceed 0.50 from 05:00 to 14:00. Generally, the variations of CSI (Figure 8c) still demonstrate that FY-2G QPE outperforms IMERG and FY-2E QPE, with the largest CSI values during the entire day, while the CSI values of FY-2E QPE are the smallest. All of the three satellite-based precipitation products have values of FBI larger than one (Figure 8d), which indicates that all products tend to overestimate precipitation occurrences over the study area.

**Figure 8.** Temporal patterns of performances of FY-2E QPE, FY-2G QPE, and IMERG in terms of (**a**) POD, (**b**) FAR, (**c**) CSI, and (**d**) FBI against ground observations, respectively.

Figure 9 illustrates the numerical distributions of contingency statistical indices for FY-2E QPE, FY-2G QPE, and IMERG, at daily scale. In terms of POD (Figure 9a), the performance of FY-2G QPE is close to that of IMERG, with mean values of around 0.87 and 0.82, respectively, while the mean value of POD for FY-2E QPE is around 0.62. For the distributions of FAR (Figure 9b), the mean value of FY-2G QPE is the smallest (around 0.25), while the mean values of IMERG and FY-2E QPE are both around 0.4. In spite of the well-performing median, FY-2E QPE shows the worst POD and FAR distributions, since the range of whiskers is too large compared with that of the other two products. Regarding CSI (Figure 9c), it shows similar distributions and numerical characteristics to those of POD, which indicates that FY-2G QPE outperforms IMERG and FY-2E QPE, with the largest mean value of around 0.6. For FBI (Figure 9d), the mean values of all three precipitation products are larger than 1, which indicates that each of the three products shows a tendency to overestimate precipitation occurrences at diurnal scale. The mean values of FBI of the FY-2 series satellite precipitation products are closer to one than IMERG, indicating a smaller degree of overestimation. Note that some FBI values of FY-2G QPE are smaller than one, which indicates that FY-2G QPE underestimates precipitation occurrences in some areas.

FG **Figure 9.** The numerical distributions of contingency statistics for FY-2E QPE, FY-2G QPE, and

IMERG

#### in terms of (**a**) POD, (**b**) FAR, (**c**) CSI, and (**d**) FBI, respectively.

#### *4.5. Cross-Evaluation of FY-2 Precipitation Products Based on IMERG*

Figure 10a–c demonstrate the inter-comparison results for FY-2E, FY-2G precipitation products, and IMERG, in terms of the total precipitation in summer, 2018. The number of pixels involved in cross evaluation between FY-2E QPE and IMERG is di fferent from the number between FY-2G QPE and IMERG, which is mainly caused by the di fferent ratios of data absence of FY-2E QPE and FY-2G QPE in northwestern mainland China. It is obvious that the correlations between FY-2G QPE and IMERG (CC of ~0.81) are much larger than those between FY-2E QPE and IMERG (CC of ~0.29), which is mainly caused by some significant overestimates of FY-2E QPE for the total precipitation in summer, when the precipitation is relatively small, compared with IMERG data. Additionally, the values of CC and other indicators between FY-2E QPE and FY-2G QPE are relatively poor, which indicates that the estimates of FY-2E are somewhat unreliable. Overall, according to the inter-comparisons displayed in Figure 10, FY-2G QPE agreed better with IMERG than FY-2E QPE in terms of spatial patterns and consistency.

**Figure 10.** The inter-comparisons of (**a**) FY-2E QPE and IMERG, (**b**) FY-2G QPE and IMERG, and (**c**) FY-2G QPE and FY-2E QPE in terms of total precipitation in summer, 2018.
