## 4 Discussion
### The Model Comparison
It claims that these variables at local scale do not have strong connections with the raw ERA5-Land data. The linear models with the ERA input dataset work better than the raw or debiased ERA5-Land data. The median model ranks are 83 and 80 for the LM and LMI models, respectively. The LM model showed the best results of all models in the _KGE_ metric. An increase in the linear model complexity after implementation of interactions (LMI model) does not increase the model's performance. Two tested Gaussian process regression models demonstrated different results. The model with a rational quadratic kernel has medium rank values (_MER_ = 81) in contrast with the GPR model with exponential kernel, which has the highest rank (_MER_ = 92). [PERSON] [15] concluded that multiple linear regression shows successful results for nowcasting tools in micrometeorology. The presented results \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & **Dataset** & _CC_ & _NRME_ & _KGE_ & **Rank _CC_** &
\begin{tabular}{c} **Rank** \\ _NRMSE_ \\ \end{tabular} & **Rank _KGE_** & _MER_ \\ \hline ERA & ERA & 0.89 & 10.01 & 0.70 & 35 & 0 & 0 & 12 \\ dbERA & ERA & 0.89 & 8.49 & 0.78 & 35 & 65 & 61 & 54 \\ LM & ERA & 0.90 & 8.08 & 0.83 & 66 & 83 & 100 & 83 \\ LMI & ERA & 0.90 & 7.99 & 0.80 & 77 & 87 & 77 & 80 \\ GPRexp & ERA & 0.91 & 7.68 & 0.80 & 100 & 100 & 77 & 92 \\ GPR2 & ERA & 0.90 & 7.95 & 0.82 & 66 & 88 & 89 & 81 \\ NN & ERA & 0.90 & 7.75 & 0.80 & 77 & 97 & 78 & 84 \\ LM & PCA & 0.88 & 8.55 & 0.79 & 26 & 63 & 67 & 52 \\ LMI & PCA & 0.88 & 8.21 & 0.79 & 27 & 77 & 67 & 57 \\ GPRexp & PCA & 0.90 & 8.05 & 0.75 & 70 & 84 & 41 & 65 \\ GPR2 & PCA & 0.87 & 8.64 & 0.82 & 0 & 59 & 89 & 49 \\ NN & PCA & 0.90 & 8.36 & 0.81 & 69 & 71 & 80 & 74 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the model performances for the validation subset from ERA and PCA datasets. _MER_—mean rank. do not support the relevance of the multiple linear regression model for simulation of meteorological variables. The neural network model showed a high relation with the in situ observed data. _NRMWE_ was close to the best model results, and the NN model was in the second place according to _MER_ = 84. An artificial neural network of multilayer perceptrons was more effective than the multiple linear regression for gap-filling of air temperature and relative air humidity in Brazil [14]. Principal component analysis is an effective technique for the reduction of data dimensions [56]. Highly correlated time series can be replaced by a small number of new variables describing the general features of the time series. Application of the first 25 principal components constructed from the 47 initial input variables reduces the model complexity. Unfortunately, the model performance is also decreased. All five tested models based on the PCA dataset demonstrated a lower _MER_ than models based on the ERA dataset. Some insignificant increase in the _KGE_ metric was registered only for the GPRexp and NN models. The Gaussian process regression model with an exponential kernel based on the ERA dataset demonstrated the maximal _CC_ and the minimal _NRMSE_ metrics compared to all other tested models. The _KGE_ complex metric for the GPRexp model is also high. Comparison of the median metric allowed us to conclude that the GPRexp model should be used for construction of the gap-free time series of meteorological variables. Figure 9 shows the comparison of the observed and modelled meteorological variables from the GPRexp model based on the ERA dataset. The matching of observed and modelled time series is much better than for other models (see Figure 4). The difference between modelled and observed data for the training set is 10-100 times lower than for the validation dataset, except the modelling results for wind direction (see panel 6 in Figure 9). Meteorological variable data obtained from direct observations and reconstructed from the GPRexp model (Figure 1) demonstrate reasonably coinciding diurnal and annual dynamics.