## 3 Results and Discussion
### Error Metrics at the All-India Scale  
In this section, error characteristics of IMERG products have been assessed at all-India scale against the IMD rain-gauge-based dataset. Daily all-India mean rainfall estimates for the monsoon season (e.g., June-September) from both multi-satellite and rain-gauge-based data have been compared for the 4-year period. Table 3 shows the different error metrics for IMERG products at the all-India scale. Bias and RMSE are normalized with respect to the mean rainfall obtained from the rain-gauge-based dataset and presented in percentage. All IMERG products overestimate all-India mean monsoon rainfall as compared to the rain-gauge-based dataset. Bias in near-real-time IMERG products is about 2-3 times more than the research product. Biases in all-India daily mean monsoon rainfall are 10-11% in near-real-time IMERG products, whereas it is about 3% in the research product for 2014-2017. IMERG-E shows about 1% higher bias than IMERG-L. IMERG-F V6 has a marginally larger bias than IMERG-F V5. However, IMERG-F V6 exhibits about double bias during the pre-GPM period (2010-2013) than the GPM period (2014-2017). Bias in all-India daily mean monsoon rainfall in IMERG-F V6 is about 3% during the GPM period, whereas it is about 6% during the pre-GPM period. Temporal CV indicates that the IMERG-F V6 product is in good agreement with rain-gauge-based dataset in the detection of daily rainfall variability, while near-real-time products slightly overestimate the all-India daily mean rainfall variability. The correlation coefficient, RMSE, and KGE clearly indicate that IMERG-L is marginally better than IMERG-E, and IMERG-F V6 is superior to near-real-time IMERG products. Normalized RMSE is the minimum of about 17%, and correlation coefficient and KGE are the largest for the IMERG-F V6 during the GPM period. The improvement in these metrics is marginal in IMERG-F V6 as compared to IMERG-F V5. Moreover, a better performance of IMERG-F V6 during the GPM period than the pre-GPM period is also evident. These results clearly indicate that IMERG-F V6 during the GPM period performs better than near-real-time, V5, and pre-GPM IMERG period products at all-India scale. Figure 7 shows the spatial distributions of correlation coefficient and KGE for IMERG products as compared to the rain-gauge-based dataset across India. A smaller correlation coefficient in IMERG products has generally been found over the northern and northeastern parts of India. However, the research product of IMERG shows a higher correlation coefficient than near-real-time products. Both V5 and V6 estimates of IMERG-F show a similar correlation coefficient, but IMERF-F V6 has a larger correlation with the rain-gauge-based dataset during the GPM period than the pre-GPM period. The KGE score combines correlation, bias, and variability and gives equal weightage to each of the components. Therefore, a smaller KGE score has been seen in IMERG products over larger parts of the northern and northeastern parts of the country. It is also to be noted that the rain gauge density over the northern and northeastern parts is rather poor [(63)], and rain gauge density plays a key role in the evaluation of any satellite-based precipitation product. Therefore, the error metrics over these sub-regions might have larger uncertainty. The pattern of KGE is similar to the bias pattern over the west coast in IMERG-F estimates. IMERG-F shows better performance than IMERG-E and IMERG-L. Overall, both near-real-time IMERG products have similar error characteristics in precipitation estimation. There is no noticeable change