# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 3 Results and Discussion
### Assessment Based on Rainfall Intensity  
The objective of this evaluation was to explore the performance of the IMERG and ERA5 precipitation products in detecting different precipitation intensities (Table 5). For the first class (0 \(\leq\) T \(<\) 1 mm), the GPM-F, GPM-L, and GPM-E models show similar performances with high median accuracy values (0.88 to 0.89) and F1_scores of 0.94 (Figure 10). This suggests that these methods are accurate in predicting low-intensity precipitation and have a good balance between recall and precision. However, the ERA5 product performs differently, exhibiting higher precision (0.98) but slightly lower recall (0.82), resulting in a lower F1_score (0.89). This indicates that the ERA5 product is more likely to correctly predict positive values but may miss some positive instances. The ERA5 product also has a lower false discovery rate (FDR) (median value of 0.02) compared to the GPM products (median value of 0.06), indicating a lower rate of false positives. However, for the remaining four classes representing higher precipitation intensities, all tested products show relatively low performances, with median accuracy values ranging from 0.1 to 0.24. The recall values are also low, indicating that the products struggle to detect positive data accurately. There is a trend of improvement in recall as the precipitation intensity increases, but the values remain relatively low for all classes. The precision values are also low, suggesting a high number of false positives. This is further confirmed by the high FDR values, indicating a high rate of false positives for all products. The F1_score, which combines precision and recall, is also low, ranging between 0.18 and 0.39 for the four classes (Figure 10). Overall, the results show that the GPP's performance is stronger for low-intensity precipitation. The GPM products perform relatively better than the ERA5 reanalysis product in terms of overall accuracy and balance between recall and precision for low-intensity precipitation. However, all products face challenges in accurately predicting and detecting higher-intensity precipitation events. The regression metrics provide insights into the performance of the GPPs in terms of bias, RMSE, MAE, and correlation coefficients for different precipitation intensity classes. Regarding bias, all products overestimate precipitation for the first class (0 \(\leq\) T \(<\) 1). The GPM models have a lower bias compared to ERA5 (median of 0.03 and 0.07, respectively). For the second class (1 \(\leq\) T \(<\) 5), ERA5 overestimates precipitation (median bias of 0.32), while the GPM models underestimate it, with median biases ranging from \(-\)0.19 to \(-\)0.25. For classes 3 and 4 (\(\leq\) T \(<\) 20 and 20 \(\leq\) T \(<\) 40), ERA5, GPM-F, and GPM-L show an overestimation of precipitation, while GPM-E underestimates it. For the last class (T \(\geq\) 40), all products overestimate precipitation, with GPM-F having the lowest bias. In terms of RMSE and MAE errors, GPM-E and GPM-L models have the lowest values for classes 1 and 2, followed closely by GPM-F and ERA5. GPM-L has the lowest errors for class 3, GPM-E for class 4, and GPM-F for the last class. The results indicate that RMSE and MAE errors, as well as bias interval variations, increase from the first class to the last class for all products, indicating a decrease in accuracy as precipitation intensity increases (Figure 11). Figure 11: Boxplots of regression metrics for the different intensity classes (Red line: Median; Dots: Outliers). Figure 10: Boxplots of classification metrics for the different intensity classes (Red line: Median; Dots: Outliers). When examining correlation coefficients, very low values ranging from 0.00 to 0.25 were observed for all products, except for the last class where some improvement in consistency was observed (Figure 11). This indicates that the products have limited ability to capture the spatial patterns of precipitation for the earlier intensity classes, but show some improvement for the last class.