# Evaluation of IMERG Precipitation Products in the Southeast Costal Urban Region of China
## 4 Results
### Evaluation for Different Precipitation Intensities  
Figure 4 shows the changes in each metric for different precipitation intensities on an hourly scale. For accuracy metrics, \(MBE\) of all three IMERG products had large negative values when precipitation intensity was higher than 2.5 mm/h, indicating the overall drier biases of IMERG estimates for high-intensity precipitation. \(Corr\) fluctuated at different intensities, but the best scores always occurred in the range of 0.1-8 mm/h. Sensitive to the estimate magnitudes, \(RMSE\) had larger values at higher intensities. The detection abilities of all three IMERG products were closely related to the precipitation intensity. \(POD\) significantly decreased with increasing intensity, while \(FAR\) had a U-shaped change. This showed that there existed several cases in which IMERG overestimated the intensity level of no or weak precipitation events. Meanwhile, IMERG also tended to misidentify the intensity levels as heavy or torrential rain while underestimating the level of actual high-intensity precipitation. As a result, all three IMERG products had relatively good detection abilities for moderate-intensity precipitation, as shown by the high \(ETS\) scores in the precipitation intensity range.  
The changes of \(MBE\), \(RMSE\), \(POD\) and \(FAR\) with precipitation intensity for the three IMERG products on a daily scale in Figure 5 are similar to those on an hourly scale. However, the two metrics of \(Corr\) and \(ETS\) exhibit an overall increasing trend with the precipitation intensity. This could stem from the fact that the six intensity levels on hourly and daily scales do not correspond exactly to each other in proportion. In other words, this may be due to the deficiency of the IMERG products in describing the diurnal variation of persistent, high-intensity precipitation. The performance differences between the IMERG products on a daily scale are mainly reflected in the detection of high-intensity precipitation, while there is not much difference in the comparison of weak precipitation.  
Figure 4: Changes in (**a**) \(MBE\), (**b**) \(Corr\), (**c**) \(RMSE\), (**d**) \(POD\), (**e**) \(FAR\), and (**f**) \(ETS\) for different precipitation intensity levels at hourly scale.  
Many evaluations have reported the overestimation of weak precipitation and underestimation of heavy precipitation by IMERG [46; 70; 71; 72; 73]. The overestimation of weak precipitation largely comes from the misdetection of non-precipitation events (Figures 2, 4e and 5e), which is because the passive microwave (PMW) sometimes misidentifies dynamic land surface characteristics as precipitation signals [74]. In addition, the IMERG algorithm (especially the morphing process) is also noted to increase the occurrence of estimated rainfall [51]. For high-intensity precipitation, some researchers consider that the underestimation of IMERG stems from the inherent limitations of PMW and infrared (IR) estimation algorithms ([PERSON] et al., 2019). PMW-based and IR-based algorithms estimate land precipitation from the scattering signal of frozen hydrometeors and cloud top temperature, respectively [75; 76; 77]. Therefore, they have difficulty in accurately capturing heavy precipitation (such as convective storms) that are weakly linked to cloud top temperature or have no/few ice particles. The Bayesian averaging in the Goddard profiling (GPROF) retrieval algorithm would also lead to underestimation of high-intensity precipitation [78]. Specifically, the Bayesian method averages multiple profiles in the priori database, which makes the most extreme heavy precipitation in the database to be averaged to lower values and unretrievable.