# Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda
## 4 Conclusions  
This study focused on the performance of the GPM-IMERG- Early-, Late-, and Final-run satellite precipitation products during the Tropical Storm Imelda. Categorized as a tropical storm at midday on 17 September by the National Hurricane Center (NHC), Imelda made landfall with massive downpours during 17-19 September 2019. The spatial distribution and temporal evolution of the storm during 17-20 September showed that the storm caused two major peak precipitations; one on 18 September and one on 19 September. The quality of IMERG products at hourly and 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) temporal resolutions during the storm was evaluated using Stage-IV radar measurements as a reference. The hourly Stage-IV radar product was calibrated and adjusted using rain gauge networks in the surrounding study area. The timeframe of the data was adjusted to include three days before the landfall and three days after the dissipation to better capture the temporal and spatial variability of the event. Generally, areas that received a higher amount of precipitation were found to have relatively good statistical metrics by all three of the IMERG GPM products. Maps of the CC showed that the alignment of the IMERG and radar was not as good as other studies have shown, with an average of 0.60 for the best product [53; 62; 70]. Moreover, the coefficient of determination (R\({}^{2}\)) for all three IMERG products was just 0.4. When it comes to RMSE, three-quarters (ranging from 73% to 76%) of the area experienced an RMSE of less than 5 mm in all three IMERG products. The IMERG GPM Final-run product showed a huge overestimation  
Figure 13: Spatial distribution of PSS for the IMERG (**A**) Early-, (**B**) Late-, and (**C**) Final-run products during Tropical Storm Imelda. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study Period** & **Product** & **POD** & **FAR** & **CSI** & **PSS** \\ \hline
14-21 & Early & 0.87 & 0.38 & 0.57 & 0.68 \\ September & Late & 0.90 & 0.41 & 0.56 & 0.68 \\
2019 & Final & 0.91 & 0.44 & 0.53 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The areal average of the probabilistic statistical indices of the satellite products. of the storm, which was confirmed by basic statistical indices. The spatial distribution of the RBIAS indicated similar patterns in the near-real-time IMERG products (Early and Late). However, the gauge-adjusted IMERG product (Final-run) showed the very high overestimation and underestimation across the affected region varied by location (inland and coastal areas). Significant underestimation was found in the hard-hit coastal areas, whereas significant overestimation was recorded in the relatively less-hit inland areas. This summarizes the weakness of satellite-based precipitation products; it has been reported in the literature for a long time that the IMERG products underestimate precipitations during extreme (heavy) events and overestimate average to low events [70; 71; 72; 73; 74; 75]. It is also notable that during summer and fall months, IMERG products have been found to show dry biases for convective systems [44]. The Early-run showed a much better RBIAS relative to the Final-run product. However, when considering the overall performance of the IMERG products, it was poor, because areas with an acceptable range of RBIAS (i.e., between \(-10\%\) and \(10\%\)) in all the three IMERG products were only about \(16\%\) to \(17\%\) of the total area. Probabilistic statistical indices showed that all three products were successful in capturing the precipitation event with adequate accuracy (average POD \(\approx 0.9\)). FAR index results were less strong, with the Early- and Late-run products having almost \(67\%\) of the areas with a FAR <0.4; however, only \(40\%\) of the areas in the Final-run product showed a FAR <0.4. PSS and CSI scores showed that the IMERG GPM Final-run product was outperformed by the near-real-time IMERG products (Early and Late) in capturing the entire storm event with better accuracy.