# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 5 Discussion  
The main finding from this study is that SMP product dramatically decrease RMSE values compared with the original GPM product. Further linear and nonlinear fitting correction bring additional improvement, whereas the CDF fitting correction do not bring substantial improvement compared with SMP. The results underscore the importance of using satellite soil moisture data as a completely independent information source to effectively correct the accumulated errors of the near-real-time rainfall products. The results also emphasize that the SMP product can be further corrected using suitable fitting methods. Note that the calibration period for parameter derivations in the fitting methods are less than two years due to the short history of the SMAP and GPM products. The correction might become more effective with a longer calibration period. Therefore, we expect a better near real-time satellite precipitation product when longer data records from SMAP and GPM become available. The choice of the fitting method is also important to improve the satellite products, with the linear correction being the most effective. The nonlinear fitting method is slightly worse than the linear fitting method in describing the relationship between the GPM-based precipitation and gauge observations. The Gamma distribution may not reflect precipitation characteristics in the CDF fitting method. The effect of correction algorithms depends on many factors, such as the quality of the original GPM product, soil properties, soil states, the structure and parameters of the API model. Of them, the accuracy of soil moisture observation determines whether the assimilation methods can improve the satellite products and by how much. While we do not conduct a quality assessment of SMAP observations using in situ soil moisture measurements, we may have implications on the SMAP performance based on LAI values. Figure 6 presents the relative difference in RMSE between GPM and SMP with different ranges of LAI. The median value of RMSE decrease from GPM to SMP is more than 20% for LAI ranging from 1 to 2, and this improvement becomes less effective as LAI increases. Although L-band measurements used in SMAP are less sensitive to vegetation than C-band measurements due to the low noise produced with SMAP [66], the SMAP soil moisture is still substantially biased over vegetated areas [67; 68]. The lower quality of SMAP soil moisture over vegetated areas may lead to less improvement from GPM estimates to SM corrected precipitation. Another possible factor affecting the improvement from GPM to SM corrections could be the frequency of the SMAP satellite overpass. Unlike the satellite data on precipitation, soil satellite data often take several days (depending on the location) to have an observation data. Figure 7 shows the frequency of the SMAP observations in China for 2017, which shows clearly more SMAP observations in the southeast and northwest than northwest and southwest. The correction seems more effective in Southeastern and Northwestern China than other regions, because the correction effect is partly determined by how many soil satellite data points can be used in the assimilation period. More observations of satellite soil moisture may further correct the deviation of satellite precipitation in the assimilation algorithms. In addition, the larger number of SM observations probably results in shorter assimilation windows, which also can make a better improvement. We note that there are other types of bias correction methods in correcting the satellite precipitation product. For example, [PERSON] et al. [69] adopted a Bayesian method in improving two satellite products, and showed that the national average of the mean error was reduced by 70-100%. Note that the performance metric used in our study is the median of the absolute error, not the mean error. For comparison with the [PERSON] et al.