# Evaluation of IMERG Precipitation Products in the Southeast Costal Urban Region of China
## 6 Conclusions  
Precipitation is a key factor linking atmospheric and terrestrial processes. It is important to obtain accurate information for precipitation distribution and variability, especially for urban areas that are affected more significantly by precipitation-related disasters. The southeast coastal region of China is a typical coastal urban agglomeration, which is econom  
Figure 10: Summer \(POD\) for IMERG ER (on (**a**) hourly and (**d**) daily scales), IMERG LR (on (**b**) hourly and (**e**) daily scales), and IMERG FR (on (**c**) hourly and (**f**) daily scales) along with elevations. ically active and densely populated. Frequent strong convective weather events, such as typhoons and floods, plague the region, so timely and reliable precipitation information is very important. In recent years, various SPPs have been developed to provide wide-range and continuous precipitation estimates. As a high-quality precipitation dataset with fine spatial and temporal resolution, IMERG has great potential for applications in urban rain-storm warning, disaster prevention, and health management. Therefore, it was necessary to evaluate the performance of IMERG in this coastal urban region. With gauge observations from a local high-density network, this paper presents a comprehensive evaluation of the three runs of IMERG (IMERG ER, IMERG LR, and IMERG FR). The evaluation was conducted at multiple temporal (hourly and daily) and spatial (regional and grid) scales in terms of estimation accuracy and detection ability. The conclusions are as follows. 1. All three IMERG products severely overestimate weak precipitation and underestimate high-intensity precipitation, which somehow leads to the better estimation of moderate-intensity and relatively heavy precipitation at sub-daily and daily scales, respectively. 2. Among the three IMERG runs, IMERG FR has the best performance, but is not that different to IMERG ER and IMERG LR. 3. In different seasons, IMERG performances, in order of best to worst, are found in summer, spring, autumn and winter. Compared to IMERG ER and IMERG LR, the calibrated IMERG FR exhibits a more consistent performance across seasons. 4. The estimation ability of IMERG is best in coastal urban areas in summer and northern forests in winter. Due to the year-round urban impact on precipitation variability, IMERG cannot detect urban precipitation occurrences well, which makes IMERG better at detecting precipitation on cultivated lands in summer and forests in winter. 5. Within the urban regions, IMERG performs better in areas with lower urbanization levels. 6. Elevation has a significant negative effect on the ability of IMERG to capture summer precipitation occurrences. In summary, the detection capability of IMERG needs to be greatly improved for the southeast coast of China. The current IMERG version is not recommended for use in sub-daily-scale urban research within the study area, such is the urban effect on precipitation and the urban weather nowcast. Future improvements could involve a regionalized segmental correction to the satellite estimates based on precipitation intensity. Besides, for the research-level IMERG FR, an attempt could be made to adjust the SPP estimates with daily or sub-daily observations to obtain more accurate local short-term precipitation information. This evaluation provides a helpful reference for the application of IMERG products in coastal urban areas. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14194947/s1](https://www.mdpi.com/article/10.3390/rs14194947/s1)]([https://www.mdpi.com/article/10.3390/rs14194947/s1](https://www.mdpi.com/article/10.3390/rs14194947/s1)). This work was supported by the National Natural Science Foundation of China (grant no. 41971312, 41890854). The rain gauge observations were provided by the Meteorological Unified Service Interface Community (MUSIC). The IMERG and GlobeLand30 datasets are available via the respective websites of the dataset producers.