# Spatiotemporal Assessment and Correction of Gridded Precipitation Products in North Western Morocco
## 3 Results and Discussion
### Bias Correction Results  
According to the results obtained from regression and classification metrics, the GPM-F product outperforms all the other GPPs at various scales. Previous studies have also shown that the GPM-F product outperforms other products. For example, in Saudi Arabia, [86] compared the three versions of the IMERG product and concluded that GPM-F was the most accurate and could be used in conjunction with ground measurements. In Iran, ref. [87] evaluated four products and found that GPM IMERG and TRMM were more accurate than Chirps in estimating precipitation amounts. Similar results were found in China, Malaysia, and Pakistan by [88; 89; 88] using the GPM-F product. Furthermore, [PERSON] et al. [19] evaluated six satellite products in sub-Saharan Morocco and demonstrated that GPM-F had the highest correlations and the lowest MAE and RMSE for all time scales.  
Figure 21: Frequencies of the different intensity categories for each month and each precipitation product for the pixel average.  
Figure 20: Ability of each product to describe average daily precipitation for the pixel average.  
In order to improve the accuracy of the GPM-F model, we have applied a bias correction. Figures 22 and 23 illustrate the regression and classification metrics of the GPM-F model before and after the bias correction. These results are derived from a test conducted on the artificial neural network (ANN) model constructed and trained for this study. Following the correction, the results show a significant reduction in biases and in RMSE and MAE at all time scales. Additionally, there is a slight improvement in the coefficients of determination and correlation at the daily and annual scales, but insignificant improvement at the monthly scale.  
At the daily scale, the correction resulted in a significant decrease in the median bias, from 0.18 to 0.02, as well as the median RMSE and MAE, from 4.17 and 1.19 to 0.76 and 0.25, respectively. The correlation increased from 0.64 to 0.65, and \(R^{2}\) increased from 0.41 to 0.42. At the monthly scale, the correction also led to a significant decrease in the median bias, from 6.04 to 0.012, as well as the median RMSE and MAE, from 18.71 and 11.83 to 0.35 and 0.93, respectively. However, no significant improvement in correlation was observed at this scale. At the annual scale, the correction resulted in a considerable reduction in the median bias, from 72.44 to 0.02, as well as the median RMSE and MAE, from 98.86 and 80.64 to 0.42  
Figure 23: Boxplots of classification metrics before and after bias correction (Red line: Median; Dots: Outliers).  
Figure 22: Boxplots of regression metrics before and after bias correction (Red line: Median; Dots: Outliers).  
and 0.36, respectively. The correlation increased from 0.88 to 0.91, and \(R^{2}\) increased from 0.77 to 0.83 (Figure 22).  
After the bias correction, the classification metrics showed a significant improvement in the overall performance of the model, with an increase in accuracy from 0.75 to 0.87, indicating a better ability to correctly predict the correct class for each observation. Precision also increased from 0.38 to 0.60, suggesting that the model is capable of predicting the positive class with greater accuracy and identifying true positives more precisely. However, recall decreased from 0.81 to 0.61, indicating that the model has a lower ability to identify all true positives, and the number of false negatives increased. The F1_score increased from 0.52 to 0.60, indicating an overall improved performance in terms of precision and recall. However, the AUC-ROC decreased from 0.78 to 0.76, suggesting a slight reduction in the model's ability to discriminate between positive and negative classes. Finally, the FDR decreased from 0.62 to 0.4, indicating a decrease in the percentage of incorrect positive predictions, demonstrating the model's improved ability to minimize false positives (Figure 23).