# Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda
## 3 Results and Discussion
### Basic Statistical Indices
In all of the three IMERG products, about three quarters (ranging from 73% to 76%) of the area experienced an RMSE of less than 5 mm in estimating the Tropical Storm Imelda. [PERSON] et al. [44] conducted a study over the central and eastern United States comparing IMERG products with ground-based observations and found that the IMERG products showed the largest RMSE values during summer and fall months, caused by severe underestimation. They attributed this error to dry biases of the IMERG algorithm for precipitation estimation of convective systems. Figure 8 illustrates the spatial variability of the relative bias (RBIAS) over the study area during Tropical Storm Imelda. The spatial distribution of the RBIAS shows similar patterns for the near-real-time IMERG products (Early and Late). However, the spatial pattern was different for the Final-run product, which had a latency of about 3.5 months (Figure 8). The inland areas show much higher positive RBIAS (overestimation) in the Final-run product than the two earlier products, especially in counties such as San Jacinto, Polk,  
Figure 6: Scatterplot of IMERG (**A**) Early-, (**B**) Late-, and (**C**) Final-run products, and Stage-IV average hourly precipitation data from 14 to 21 September 2019.  
and Fort Bend, where the positive RBIAS was more than 75%. This might be attributed to the tendency of IMERG Final-run products to overestimate precipitation measurements in inland areas during extreme storm events [68; 69]. With the Early- and Late-run products, the coastal areas show a higher positive RBIAS and the inlands show a lower negative RBIAS. Overall, the Early- and Late-run products underestimated the tropical storm, with mean RBIAS values of \(-4\%\) and \(-11\%\), and medians of \(-15\%\) and \(-18\%\), respectively. However, the Final-run product showed contrasting results by providing a mean RBIAS of 41% and a median RBIAS of 20%. This suggested that the Final-run product overestimated the tropical storm by a large margin. However, when we follow the spatial distribution of the RBIAS (Figure 8C), it is very clear that the Final-run product significantly overestimated precipitation in less-hit areas. In contrast, it underestimated precipitation in the hard-hit areas. For all three products, about 50% of the areas experienced RBIAS between \(-30\%\) and \(30\%\). Hence, it can be said that the performance of the IMERG Final-run product was not as good as the Early- and Late-run products in capturing the storm event. In more than 30% of the study area, the Final-run product indicated an overestimation of the storm event by more than 50%. However, the Late and Early product estimates showed an overestimation of more than 50%, in 8% and 12% of the study area, respectively. This suggest that there was a significant problem of overestimation in the Final product, especially in the outskirts of the study area (Figure 8C). The areas with an acceptable range of RBIAS (i.e., between \(-10\%\) and \(10\%\)) in all the three IMERG products were about 16% to 17% of the total area. This outcome follows similar ones from earlier studies, where GPM products underestimated precipitation measurements during extreme events [35; 68; 69; 53]. Table 2 summarizes the performance of the three IMERG-GPM products based on basic statistical indices for the study period. Figure 8: Spatial distribution of RBIAS for the IMERG (**A**) Early-, (**B**) Late-, and (**C**) Final-run products during Tropical Storm Imelda. Figure 7: Spatial distribution of RSME for the IMERG (**A**) Early-, (**B**) Late-, and (**C**) Final-run hourly products during Tropical Storm Imelda. The Kling-Gupta model efficiency coefficient (KGE) shows a spatial distribution pattern similar to the other basic statistical indices above.