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Uncertainty of model predictions was relatively large for low intensity rainfall, which highlights one of the limitations of this technique.
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His predictions are relatively conservative.
That has worked for the standard model because its predictions are relatively clear.
A sensitivity analysis revealed that the predictions are relatively more sensitive to the equilibrium constant and the mass ratio than to Peclet and modified Sherwood numbers.
The model predictions are relatively insensitive to changes in the nonsolvent/polymer and nonsolvent/solvent friction coefficients but moderately sensitive to changes in the solvent/polymer friction coefficient.
Model bias and normalized root mean square values for precipitation predictions are relatively small, but there are significant differences when comparing modeled and observed probability density functions for precipitation in EC and YRD.
(i) Compared to RANS, which require evidence for their predictions, the LUM has the significant advantage that the quality of predictions is relatively independent of the RANS model applied.
The predictions are relatively better for winter season, for which slope is 0.8145, intercept is 0.0836, and R2 = 0.7543.
In silico approaches, often used for early predictions, are relatively less costly and less time involved, when compared to standard experimental approaches [9, 15].
It may be seen from this Table that while the raw numerical estimation is associated with low R and high RMSE and MAE the predictions are relatively much satisfactory up to 5 days in future.
The predictions are relatively poor against summer data as shown in Fig. 13a, for which the linear regression plot shows a slope of 0.597, an intercept of 0.0792, and an R2 = 0.619.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com