The authors present a digital shadow Kalman filtering method based on the direct linearization of a trusted multibody aeroservoelastic model of a wind turbine. The approach leverages validated models to reduce development time and improve confidence in results. Building on previous efforts, the authors enhance the formulation by including additional structural dynamics, such as tower side-side and blade flapwise/edgewise modes, and by integrating real-time inflow estimators for rotor-equivalent wind speed, shear, and yaw misalignment. These inputs dynamically schedule the internal model to reflect current turbine conditions. To further improve accuracy, the model is augmented with data-driven corrections via bias and neural network methods. The approach is validated through simulations and field data, demonstrating robust performance even in complex inflow scenarios, with damage equivalent load estimation errors reduced to just a few percent after correction.

The manuscript conveys an interesting and well-founded message, but several improvements should be considered before final acceptance in Wind Energy Science:

1) The abstract, introduction, and main body of the paper are quite long, which can make it difficult for readers to follow the key contributions and technical details. Consider condensing these sections to improve readability and focus.

2) The statement "First, the static force term $f_0$ was modified by trial and error until no further improvement was possible." suggests an ad hoc tuning approach. Could the authors clarify whether a more systematic or automated optimization method (e.g., gradient-based, Bayesian, or heuristic optimization) could be applied here?

3) A brief discussion comparing the short-term intermittency characteristics of the data-driven model with those observed in real turbine data would add value into the model’s fidelity.

4) The manuscript notes that "In all cases, the estimates track the ground-truth values reasonably well, although they fail to capture some of the higher frequency content." However, this limitation is not acknowledged in the abstract or conclusion. Highlighting it there would provide a more balanced summary of the results.

5) Finally, it would be helpful to briefly clarify in the introduction or methodology how the use of a linear Kalman filter remains effective (or is justified) in modeling the nonlinear dynamics of wind turbines.

The manuscript presents a digital shadow Kalman filtering approach based on the direct linearization of a multibody aeroservoelastic model of a wind turbine. Some mathematical models are developed, and a series of results are obtained. The topic and results are interesting, which is suitable for publishing in Wind Energy Science. However, some issues can be paid attention.

1. Some explanations of methods in Section 2 can be added, which can help to understand these methods.

2. The detailed example and input data can be illustrated in Section 3.1.

3. The English can be improved.