## 1 Introduction  
The South Aegean is an economically and environmentally significant region [1] with complex topography and physical characteristics [2]. Accurate weather and ocean-wave forecasts are essential to support a range of Digital Twin of the Ocean (DTO) applications [3], i.e., interoperable models that aim in the digital representation of the ocean physical environment, such as harbor/ship-routing safety assessment, marine pollution management, and more [4]. To enhance forecast accuracy, data assimilation (DA) techniques are employed by integrating in situ observations into numerical models [5].  
In the South Aegean, the availability of atmospheric (near) real-time in situ datasets significantly surpasses that of oceanic data. This limited availability of oceanographic data, combined with the large dimensionality and non-linearity of ocean-wave models, introduces data uncertainties that can limit the efficiency of DA [6]. Ocean circulation and wave models are particularly sensitive to wind field selection [7; 8; 9], as wind fields play a critical role in determining the momentum and heat fluxes that drive these systems. Correcting atmospheric input can potentially improve predictions of ocean circulation and wave models [10; 11]. These factors highlight the importance of DA-enhanced atmospheric forecasts.  
Numerous DA techniques exist to improve atmospheric numerical models. Among the most widely used are three-dimensional variational DA (3D-Var) [12], four-dimensionalvariational DA (4D-Var) [13; 14] and sequential methods such as the Kalman filter and hybrid methodologies combining them with variational methods [15], ensemble Kalman filter [16], and ensemble optimal interpolation [17]. Variational methods process all observations holistically, providing a more consistent and coherent estimate of the model state [14]. Sequential methods, on the other hand, process observations as they are collected, making them more adaptable to real-time data [18]. Although sequential methods can be preferred in real-time operational forecasting due to their ability to handle fluctuating data availability, they are prone to transmitting observational noise, which can reduce the precision of the estimated state [19].  
For this study, the 3D-Var method was selected as it is computationally less expensive--an important factor given the operational daily cycle [12]. It is also less demanding in terms of data requirements, making it suitable for the sparse real-time in situ datasets available in this region, and it performs well for both short-term and longer forecasting horizons [20].  
The main aim of this study was to cost-effectively correct the atmospheric forecasting model in the South Aegean for use as input in DTO applications where DA cannot be performed. The paper is organized as follows: Section 2 presents the methodology of the DA applied to the atmospheric model and details the DTO model specifics. The 3D-Var results along with the DTO model results using the corrected atmospheric forecast in comparison to the original one are presented in Section 3. Discussion of the results, along with demonstration of further DTO applications, are showcased in Section 4.