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We also analyzed other statistics (Table 1). The sum of squared errors (SSE) indicates the deviation of the responses from the fitted values of the responses. A value closer to 0 suggests a better fit. The SSEs of ERA-Interim, NCEP–DOE, MOD16, and SEBS are 147.8, 239.7, 1190, and 3701, respectively. The R-square value (R) measures how successful the fit is in explaining the variation in the data. A value closer to 1 suggests a better fit. The R value of ERA-Interim is 0.87, which is significantly higher than the root mean square error (RMSE) of this product. The RMSE represents the standard deviation of the differences between predicted values and observed values. A value closer to 0 suggests a better fit. The results show that ERA-Interim has an RMSE value of slightly less than 0.63 mm, which is lower than the RMSE values of the other products.

Table 1

Error analysis of four products vs. observations

ProductRegression equationSSE (mm)RRMSE
ERA-Interim y = (0.88 ± 0.05)X + 0.18 147.8 0.87 0.63 
NCEP–DOE y = (0.64 ± 0.07)X + 0.82 239.7 0.70 0.81 
MOD16 y = (0.66 ± 0.17)X + 4.494 1190 0.58 0.76 
SEBS y = (1.24 ± 0.97)X − 11.9 3701 0.62 0.75 
ProductRegression equationSSE (mm)RRMSE
ERA-Interim y = (0.88 ± 0.05)X + 0.18 147.8 0.87 0.63 
NCEP–DOE y = (0.64 ± 0.07)X + 0.82 239.7 0.70 0.81 
MOD16 y = (0.66 ± 0.17)X + 4.494 1190 0.58 0.76 
SEBS y = (1.24 ± 0.97)X − 11.9 3701 0.62 0.75 

SSE: sum of squares due to error, R: coefficient of multiple determination, RMSE: root-mean-square error.

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