# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 4 Results
### Performance Assessment
#### 4.2.1 Statistics
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Product**} & \multicolumn{3}{c}{**R**} & \multicolumn{3}{c}{**RMSE (mm/day)**} & \multicolumn{3}{c}{**BIAS (mm/day)**} \\ \cline{2-10}  & **10 th** & **50 th** & **90 th** & **10 th** & **50 th** & **90 th** & **10 th** & **50 th** & **90 th** \\ \hline GPM & 0.24 & 0.49 & 0.69 & 1.37 & 5.25 & 13.43 & 0.37 & 2.03 & 5.46 \\ SMP & 0.28\({}^{\text{b}}\) & 0.49\({}^{\text{b}}\) & 0.67 & 1.4 & 4.32\({}^{\text{b}}\) & 10.15\({}^{\text{b}}\) & 0.41 & 1.76\({}^{\text{b}}\) & 4.63\({}^{\text{b}}\) \\ GPM-Linear & 0.24\({}^{\text{b}}\) & 0.49\({}^{\text{b}}\) & 0.69\({}^{\text{b}}\) & 0.86\({}^{\text{b}}\) & 5.04\({}^{\text{b}}\) & 12.39\({}^{\text{b}}\) & 0.23\({}^{\text{b}}\) & 1.85\({}^{\text{b}}\) & 5.14\({}^{\text{b}}\) \\ GPM-Nonlinear & 0.32\({}^{\text{bc}}\) & 0.51\({}^{\text{bc}}\) & 0.66 & 0.86\({}^{\text{bc}}\) & 4.64\({}^{\text{b}}\) & 11.15\({}^{\text{b}}\) & 0.31\({}^{\text{bc}}\) & 2.19 & 5.61 \\ GPM-CDF & 0.27\({}^{\text{b}}\) & 0.50\({}^{\text{bc}}\) & 0.69\({}^{\text{b}}\) & 2.14 & 6.92 & 18.55 & 0.58 & 2.73 & 7.57 \\ SMP-Linear & 0.28\({}^{\text{bc}}\) & 0.49\({}^{\text{b}}\) & 0.67\({}^{\text{c}}\) & 0.67\({}^{\text{b}}\) & 4.00\({}^{\text{b}}\) & 10.40\({}^{\text{b}}\) & 0.18\({}^{\text{b}}\) & 1.69\({}^{\text{b}}\) & 4.83\({}^{\text{b}}\) \\ SMP-Nonlinear & 0.31\({}^{\text{bc}}\) & 0.49\({}^{\text{b}}\) & 0.65 & 0.70\({}^{\text{b}}\) & 4.07\({}^{\text{b}}\) & 10.52\({}^{\text{b}}\) & 0.23\({}^{\text{b}}\) & 1.81\({}^{\text{b}}\) & 5.23\({}^{\text{b}}\) \\ SMP-CDF & 0.28\({}^{\text{bc}}\) & 0.49\({}^{\text{b}}\) & 0.67\({}^{\text{c}}\) & 1.54 & 5.32 & 12.46\({}^{\text{b}}\) & 0.38\({}^{\text{c}}\) & 2.14 & 5.77 \\ \hline \hline \end{tabular} The best performance of each metric is reported in bold for products. R: [PERSON]’s correlation coefficient; RMSE: root-mean-square error, BIAS: mean absolute errors; 10 th, 50 th, and 90 th percentiles. The scores better (\(\geq\)) than the GPM. The scores of the six synergy corrected products better (\(\geq\)) than the SMP.  
\end{table} 
Table 2: Summary statistics of the spatial distribution of performance scores for the seven correction methods against CMPA. fitting corrections with the SM correction results in greater improvement than SM, linear, or nonlinear fitting correction alone in most areas of China, especially in western China, as shown in Figure 3f,g. In addition, the percentage of grid cells with improvement is the largest (79%) with the SMP-Linear or SMP-Nonlinear method, which is consistent with the smaller value of the RMSE median for the two methods. The SMP-Linear method slightly outperforms the SMP-Nonlinear method, with the smallest value of RMSE median (4.00 mm/day) and the narrowest range (0.67 mm/day for 10 th percentile, and 10.40 mm/day for 90 th percentile). By contrast, the GPM-CDF and SMP-CDF perform worse than GPM in most areas of Western China and some regions of Southern and Central China, with larger RMSE medians than the GPM. For BIAS, the median, 10 th percentile, and 90 th percentile of GPM precipitation are 0.37, 2.03, and 5.46 mm/day, respectively, as shown in Table 2. The SM correction alone reduces BIAS compared with GPM. Further correction on top of SM correction does not always lead to a smaller BIAS. The SM-linear  
Figure 3: (**a**) Root mean square error (RMSE) of daily precipitiaiton between GPM and CMPA, and (**b**–**h**) ratio of RMSE between correction method-based (SMP, GPM-Linear, GPM-Nonlinear, GPM-CDF, SMP-Linear, SMP-Nonlinear, SMP-CDF) precipitation and GPM precipitation. Warm color denotes a degradation (higher RMSE), whereas cold color denotes an improvement (lower RMSE) in (**b**–**h**). The number at the bottom-left corner of each panel denotes the percentage of improved grid cells by the correction method. The analysis period is from May 1 2017 to September 31 2017. Note that the values 0-18 on colorbar are for (**a**) GPM, and values 0-1.8 in parentheses for the ratio of RMSE in (**b**–**h**).