## 3 Results
### Model Performances  
Figure 4 presents the results of the comparison of air temperature from in situ observations and various models. Two sets of input variables were used to train the models: the ERA dataset with 47 items and the PCA dataset with 25 items. At first glance, there are no  
Figure 3: The results of direct comparison meteorological variables from observations (y) and ERA5-Land reanalysis (x). 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); blue—training subset; red—validation subset, (a) observed (y) vs. ERA5-Land data (x), (b) error (y \(-\) x) vs. ERA5-Land data (x). essential differences between the models. Direct ERA data have a systematic error of about 3.2 \({}^{\circ}\)C. The de-biased ERA data mean value is equal to the mean observed value, but the variation of differences is still high. The regression models have smaller variation of differences. The NN models have significant negative outbursts at below zero air temperatures. The comparison of average and standard deviation for meteorological variables estimated from in situ and modelled data is shown in Supplementary Tables S2 and S3. Figure 5 shows the performance metrics (_CC_, _NRMSE_, and _KGE_) for five models (LM, LMI, GPRexp, GPR2, and NN) during the testing phase (curves 1,2) and the validation phase (curves 3,4) for ERA (curves 1,3) and for PCA (curves 2,4) datasets. The dbERA model presented as points ERA at curves 3,4. The performance metrics for the testing phase are usually better than for the validation phase, due to selected calibration technique. The atmospheric pressure is reproduced by any regression models with a relatively high accuracy. The _CC_ values are above 0.99 for all models, except the NN model with the ERA dataset. _NRMSE_ is about 1-2% for the validation phase, except for the direct ERA5-Land data. According to the _NRMSE_ metric, the NN model is better than other models, but the complex _KGE_ metric demonstrates weakness of the NN model both for the ERA and for PCA datasets. The performance of air temperature models depends on selected performance metrics. The _CC_ and _NRMSE_ results in high ranks for regression models (_CC_ > 0.98, _NRMSE_ < 3%), and low ranks for the ERA (_CC_ = 0.96, _NRMSE_ = 6.6%), dbERA (_CC_ = 0.96, _NRMSE_ = 5.5%), and NN (_CC_ = 0.97, _NRMSE_ = 1%) models. The _KGE_ metric comparison results in better performance for the LM (_KGE_ = 0.96) and GPR2 (_KGE_ = 0.97) models with the PCA dataset and low performance of the NN model (_KGE_ = 0.5 and \(-\)0.6). The incoming radiation according to modelled data has a good correlation (_CC_ = 0.89 \(\pm\) 0.92) with observation data due to diurnal patterns of radiation. _NRMSE_ for most models is about 8.9-9.7% except the NN model that has an _NRMSE_ of 0.9%. The _KGE_ metric is above 0.8, but the GPR and NN models based on the PCI dataset shows lower _KGE_ than the same models based on the ERA dataset. Figure 4: Results of comparison of air temperature from observations (y) and various models (x) using training (blue) and validation (red) datasets. Models: 1—ERA, 2—dbERA, 3—LM (ERA), 4—LMI (ERA), 5—GPREexp (ERA), 6—GPR2 (ERA), 7—NN (ERA), 8—LM (PCA), 9—LMI (PCA), 10—GPREexp (PCA), 11—SPR2 (PCA), 12—NN (PCA). (a) Observed (y) vs. modelled (x), (b) error (y \(-\) x) vs. modelled (x). Figure 5: Model performance (_CC_, _NRMSE_, and _KGE_) for meteorological variables. Pa—air surface pressure (hPa), Ta—air temperature (°C), PAR—incoming photosynthetically active radiation (\(\mathrm{\SIUnitSymbolMicro mol}/\mathrm{m}^{2}/\mathrm{s}\)), RH—relative air humidity (%), WS—wind speed (m/s), WD—wind direction (deg), Rn—net radiation (W/m\({}^{2}\)), Ts—soil temperature at 5 cm (°C).