AIMS - Report and dataset from synthetic glider

The synthetic glider dataset is part of the AIMS project (Artificial Intelligence to Monitor our Seas), which aims to develop AI algorithms to improve marine monitoring by overcoming limitations of traditional methods. This dataset was created as a mitigation strategy to address delays in the experimental campaign involving the Wave Glider SV3 autonomous platform. To ensure continuity for AI model development, a synthetic dataset was generated by simulating plausible Wave Glider trajectories using outputs from the WaveWatch III (WW3). The data includes virtual glider paths designed by Politecnico di Torino (PoliTO) and CNR, with corresponding wave parameters like significant wave height, peak period, and mean direction extracted at one-hour intervals. These data points are formatted into CSV/NetCDF files consistent with the actual Wave Glider data structure. This dataset is intended to provide essential input for training AI algorithms focused on inferring wave period from sparse observations. While it serves as a crucial tool for algorithm development, its limitations include the absence of real measurement uncertainty, true sensor noise, and trajectory-induced variability.