## 4 Discussion
### The Model Comparison  
Five tested models were compared regarding their ability to reproduce the time series of meteorological variables using ERA and PCA datasets as input variables. First, the performance metrics (_CC_, _NRMSE_, and _KGE_) were averaged for eight target variables. Table 3 shows the average _CC_, _NRMSE_, and _KGE_ values for each model. Second, a rank estimation was assigned for each model and each performance metric. For instance, the GPRexp model based on the ERA dataset with the highest _CC_ = 0.91 ranked 100 for the _CC_ metric, and the GPR2 model based on the PCA dataset with _CC_ = 0.89 ranked 0. All other models ranked between 0 and 100 according to the _CC_ values. Similar ranks were assigned for the _NRMSE_ and _KGE_ metrics. Finally, mean rank (_MER_) for the _CC_, _NRMSE_, and _KGE_ parameters was used as a final and complex model estimation (Table 3). According to the suggested approach, the GPRexp model with ERA input variables demonstrated the best results for all the studied meteorological time series. It should be noted, that all performance estimations were made on an independent validation dataset not used for the model training. Figure 8: The bilayered neural network (NN) model performance (_CC_, _NRMSE_, and _KGE_) depending on the size of hidden layers (N). PCA dataset in the validation phase. 1—air surface pressure (hPa), 2—air temperature (\({}^{\circ}\)C), 3—incoming photosynthetically active radiation (\(\mu\)mol/m\({}^{2}\)/s), 4—relative air humidity (\(\%\)), 5—wind speed (m/s), 6—wind direction (deg), 7—net radiation (W/m\({}^{2}\)), 8—soil temperature at 5 cm (\({}^{\circ}\)C). The model performance for the training dataset was also estimated and is shown in Figure 5 (curves 1,2). It is clear, that the training subset demonstrates a better quality of target variables modelling than the validation subset. CC for GPRexp and GPR2 models are extremely high (above 0.995) for all target variables. The LM and LMI models demonstrate a higher _CC_ and _KGE_ for the training subset than for the validation ones. While, the _NRMSE_ for linear models does not change significantly between the training and validation subsets, the exceptions are atmospheric pressure and soil temperature variables. The _NRMSE_ value for the training dataset of the GPRexp and GPR2 models is less than 1%. A very small _NRMSE_ was also obtained for the NN model both for the training and validation subsets. Choice of ERA or PCA datasets does not impact on the NN model's performance. All models indicated a slightly lower performance during the testing phase than in the training phase. The performance for the linear model slightly improves after transition from training phase to testing phase. The performance for the Gaussian process regression model changed significantly. The NN models do not change _NRMSE_ and _KGE_ metrics, while _CC_ for the validation dataset is lower than for training for all studied meteorological variables. In general, all the models studied in this work have proven to be valid gap-filling techniques for the meteorological time series. However, some of them reconstructed the time series with greater precision. The worst coincidence was obtained between raw ERA5-Land data and the observed in situ data. The linear bias correction of the ERA5-Land data (dbERA model) improves the data reproduction quality in terms of MER from 12 to 54 (Table 3). The performance of the linear bias correction method across global sites [35] applied for ERA-Interim data reduces the mismatch with the in situ data by 10 to 36%, depending on the meteorological variable considered. The authors [35] report on the poor performance of the bias correction method for wind speed. The dbERA model tested at the present study was rather good for the wind speed variable and even for wind direction. The largest increment for the _KGE_ value was obtained after bias correction of the wind speed and soil temperature variables.