Deep learning for multi-step forecasting of stratospheric wind and temperature profiles over the Tibetan plateau

Abstract

Accurate prediction of stratospheric wind and temperature profiles is essential for understanding regional atmospheric dynamics over the complex terrain of the Tibetan Plateau. However, conventional numerical weather prediction models are computationally expensive, while statistical time-series models are limited in capturing the nonlinear evolution of atmospheric variables across both temporal and vertical dimensions. To address these limitations, MTPV-HDRNet is proposed as a fixed-site, multi-level intelligent forecasting model driven by ECMWF ERA5 pressure-level reanalysis data, with the fixed site defined as a selected ERA5 grid cell rather than an observational station. MTPV-HDRNet jointly predicts zonal wind (U), meridional wind (V), and temperature (T) for the next 24 h across 11 ERA5 pressure levels from 100 to 1 hPa, which correspond to heights of approximately 16–48 km. The model adopts a hybrid encoder–decoder architecture that explicitly represents temporal evolution and vertical stratification in a decoupled but complementary manner, thereby enhancing its ability to capture multi-lead-time profile evolution and cross-level dependencies. The model was trained using ERA5 data from 2016 to 2022, validated using data from 2023, and independently tested using data from 2024 at the fixed site in the central Tibetan Plateau. Results demonstrate that MTPV-HDRNet consistently outperforms the baseline models at all forecast lead times. On the 2024 test set, the mean RMSEs are 3.48 m s − 1 for U wind, 3.54 m s − 1 for V wind, and 1.62 K for temperature. Compared with the strongest baseline, ConvLSTM, MTPV-HDRNet reduces the RMSE by 11.9%, 11.3%, and 7.4% for U wind, V wind, and temperature, respectively. The model also maintains strong correlation performance at longer lead times, demonstrating its robustness in fixed-site atmospheric profile forecasting over complex terrain.