We became aware that one of the datasets used in our published paper, Deep Learning–Based Parameter Transfer in Meteorological Data (Farokhmanesh et al. 2023), was proprietary and that the provision of the dataset was not acknowledged properly. This oversight was unintentional and originated from a misunderstanding regarding the use of the dataset. Upon reflection and further communication with the owners of the convective-scale ensemble dataset (Necker et al. 2020) that has been used in experiments in our original publication, we realized that the data owners should be included as coauthors in the publication. The rationale behind this correction is rooted in the realization that our assumptions about the data use policy were not aligned with the expectations of the data owners. In the original publication, we cited the publication in which the dataset is presented (Necker et al. 2020) and acted in good faith that ownership rights were not violated. However, it has come to our attention that the owners of the dataset should have been acknowledged as coauthors to explicitly recognize their contribution. We acknowledge our misunderstanding of the data use policy and the inadvertent omission of the data owners as coauthors.
REFERENCES
Farokhmanesh, F., K. Höhlein, and R. Westermann, 2023: Deep learning–based parameter transfer in meteorological data. Artif. Intell. Earth Syst., 2, e220024, https://doi.org/10.1175/AIES-D-22-0024.1.
Necker, T., S. Geiss, M. Weissmann, J. Ruiz, T. Miyoshi, and G.-Y. Lien, 2020: A convective-scale 1,000-member ensemble simulation and potential applications. Quart. J. Roy. Meteor. Soc., 146, 1423–1442, https://doi.org/10.1002/qj.3744.
