"Dataset for \"HDF-Net: A Hybrid Deep Learning Model for Retrieving Ozone Profiles in the Arctic\""

"The continuous, high-temporal-resolution vertical distribution of ozone (O\u2083) in the Arctic is crucial for understanding the atmospheric chemistry and dynamics of this climate-sensitive region. However, obtaining such detailed observations remains a significant challenge. This study introduces a Hybrid Deep-learning Fusion Network (HDF-Net) to retrieve high-resolution ozone profiles by synergistically fusing ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations with meteorological reanalysis data. The HDF-Net employs a multi-branch architecture where a Convolutional Neural Network (CNN) extracts spatial features from ERA5 meteorological profiles, while parallel Bidirectional Long Short-Term Memory (BiLSTM) networks capture the sequential information from both MAX-DOAS slant column densities (SCDs) and differential slant column densities (DSCDs). These deep features are fused with solar geometry information to jointly retrieve a 25-layer ozone profile with minute-level temporal resolution, extending from the near-surface to approximately 40 km. The HDF-Net 's performance was validated using multiple independent datasets. Comparison with ozonesonde measurements reveals excellent agreement, with the correlation (r) peaking at 0.92 at 150 hPa. Crucially, retrieved near-surface ozone shows a stronger correlation with co-located in-situ measurements (r = 0.58) and a lower RMSE (6.43 ppbv) than EAC4 data. Furthermore, HDF-Net successfully captures transient dynamic events missed by the 3-hourly reanalysis product. These results establish that HDF-Net can reliably retrieve ozone profiles, providing a powerful new tool for investigating the spatiotemporal variability of ozone in the data-sparse Arctic region."