## 4 Discussion  
### Input Variables  
The gap-filling models discussed in this paper showed a good approximation of the original data. The quality of modelled data depends on the applied model and the input dataset used. The ERA dataset contains 47 meteorological variables with potentially excessive information. The taking into account of some variables is useless for improvement of the model performance. Illustration of the input variables' importance for the linear model is shown in Figure 6. The figure demonstrates changes of the model's performance depending on the number of input variables used. The order of variables is listed in Table 2. Analysis shows that air temperature is reproduced by the linear model with good accuracy using one variable (see curve 1 in Figure 6) due to the fact that the first input variable from the ERA dataset is the temperature at 2 m. The second input variable (skin temperature) slightly increases the model's performance according to the _CC_ and _NRMSE_ metrics. _KGE_ for the air temperature with the linear model varies slightly with the application of up to 30 input variables. Implementation of more variables results in a decrease in _KGE_ and reduces the model's quality. Similar changes occur with an increase in the number of input variables for other meteorological parameters. The linear model for atmospheric pressure (curve 2 in Figure 3) was improved after the addition of the 33 rd variable (surface pressure). The performance of the linear model for the wind speed (curve 5 in Figure 3) rises gradually with an increase in the number of inputs and then becomes higher with an addition of the 45 th variable (wind speed at 10 m) from the ERA dataset. Reordering of the input variable list may significantly reduce the required number of inputs for good model training. Similar results were obtained for other studied models. The _CC_ and _KGE_ for LMI, GPRexp, GPR2, or NN model increase with the rise of the number of input variables from the ERA dataset. The most significant rise of the model performance occurs after taking into account the input variable having the largest correlation coefficient with the target variable (see Table S1 in the Supplementary Materials). A further increase in the number of input variables results in a slight improvement of the model. The principal component analysis technique was applied to reduce the dimensionality of the input data and prevent preconceived notions for input variables selection. Twenty-five new variables describing the highest variability of the initial dataset were formed into the PCA dataset. The linear correlation between target variables and PCA variables is lower than for raw ERA variables. The correlation coefficients do not exceed 0.61. An increase in the number of input variables from the PCA dataset influences the overall model performance. Figure 7 shows the model performance change with the rise in the number of inputs from the PCA dataset. The 1 st PCA variable is relatively good for modelling of the air temperature (curve 1 in Figure 7). The first two input variables essentially increase the model's performance for incoming radiation and air humidity. The model for wind speed and atmospheric pressure requires at least 10-15 PCA input variables to obtain good results and reach the performance typical for other meteorological variables. Figure 6: The LM model performance (_CC_, _NRMSE_, and _KGE_) depending on the number of input variables (n) form the ERA dataset in the validation phase. 1—air surface pressure (hPa), 2—air temperature (\({}^{\circ}\)C), 3—incoming photosynthetically active radiation (\(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{m}^{2}\)/s), 4—relative air humidity (%), 5—wind speed (m/s), 6—wind direction (deg), 7—net radiation (W/\(\mathrm{m}^{2}\)), 8—soil temperature at 5 cm (\({}^{\circ}\)C). Figure 7: The model performance (_CC_, _NRMSE_, and _KGE_) depending on the number (n) of input variables form the PCA dataset in the validation phase. (**a**)—LM, (**b**)—LMI, (**c**)—GPRexp, (**d**)—NN.