## 4 Discussion  
The DA results presented in the previous section show that increasing the number of observation locations and incorporating diverse sources enhanced the realism of the downscaled model, even when using the simplest and most computationally efficient variational data assimilation method (3D-Var). The comparison with real observation data and the evaluation of forecast biases over an ensemble of three months of daily runs provide statistically significant results, with improvements observed at wind speed variable at every validation station except SAMOS that provided a consistent, gap-free data stream. More specifically, the test case examined was performed during a mostly summer period in Greece, leading to an overall reduction in wind speed bias from the control case to the 3D-Var one of 4.7%, using the improvements per validation meteorological station of Figure 4 and averaging on all stations, with a 5.6% increase during forecast hours 26-72 for the wind speed variable. For the temperature, mild improvements were noticed in Figure 5, mostly concentrated at the METAR of the Heraklion airport, which was the closest station to the denser network of assimilated meteorological stations from Data Source I (Section 2.1). A comparable reduction of approximately 4% is reported in [20] for the wind speed variable. The model enhancement using data has inherent uncertainties \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Case** & **BUOY** & **SATELLITE** & **CMEMS (Analysis)** \\ \hline \multirow{2}{*}{**NEMO SST**} & **CONTROL** & 0.52 \({}^{\circ}\)C (N = 703) & 0.62 \({}^{\circ}\)C (N = 92) & 0.62 \({}^{\circ}\)C (N = 2208) \\ \cline{2-5}  & **DA** & 0.49 \({}^{\circ}\)C (N = 703) & 0.57 \({}^{\circ}\)C (N = 92) & 0.59 \({}^{\circ}\)C (N = 2208) \\ \hline \multirow{2}{*}{**WWIII SWH**} & **CONTROL** & 0.20 m (N = 676) & - & 0.21 m (N = 2208) \\ \cline{2-5}  & **DA** & 0.20 m (N = 676) & - & 0.21 m (N = 2208) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of RMSE values for the entire validation period shown in Figure 6 for the NEMO model–sea surface temperature (SST) variable in \({}^{\circ}\)C, compared to BUOY, SATELLITE, and CMEMS (analysis model) as well as forecast values of the WWIII–significant wave height (SWH) variable. Corresponding control and data assimilated (DA) cases signify if the forcing for those models was the control case or from the WRF-DA. N is the number of samples. between studies and can be attributed to both the amount of data assimilated and the differing locations of the validation stations used in each study. As noted in [20], the data employed for DA informs the model upstream by aligning with the direction of the more dominant wind direction, which changes seasonally, considering the average dominant direction. Ideally, it is essential to identify the dominant flow direction and utilize stations positioned to convey information from the point of measurement to the point of validation. This approach enhances model accuracy for various applications. It is important to highlight that this aspect was beyond the scope of our research due to the limited number of observation stations available, even with the addition of stations from Data Source I (Section 2.1). This makes the purpose of this work more evident, as a first step to further improve realism in a regional downscaled model, also maximizing computational efficiency to be able to run it operationally with minimum cost in order to be used as forcing for ocean simulations that enable a more reliable core modelling framework for digital twin applications. Another potential increase in the accuracy of the enhanced model can be achieved by modifying the data assimilation (DA) methodology, specifically by employing the more computationally demanding and state-of-the-art 4D-Var method (Equation (2)). This method assimilates more data by considering a temporal window for data assimilation.