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Shuji Sun

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Open access Aug 2026

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

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.

Xue-cai Zhang, Zonghua Ding, Shuji Sun et al. · 0 citations
2026

PINA-Net: Physics-Constrained Nonautoregressive Network for Spatiotemporal Meteorological Forecasting in Complex Maritime Environments

Accurate regional weather forecasting in complex maritime environments is challenging due to intricate atmospheric dynamics. While deep learning presents a promising alternative to numerical weather prediction (NWP), current paradigms face an inherent tradeoff. Autoregressive (AR) models suffer from recursive error accumulation and spectral decay, whereas pure computer vision approaches neglect physical laws, yielding dynamically inconsistent predictions. To address these limitations, we propose the physics-informed non-AR network (PINA-Net) to reconcile visual sharpness with physical consistency. Our framework synergizes a lightweight 3-D spatiotemporal encoder with coordinate attention and a cumulative residual strategy, enabling the one-shot generation of high-fidelity sequences without error propagation. Crucially, we integrate a physics-constrained loss function that embeds partial differential equations (PDEs) for mass conservation and divergence directly into the optimization process. Extensive experiments on a high-resolution meteorological dataset demonstrate that PINA-Net significantly outperforms state-of-the-art baselines. The model achieves superior numerical accuracy and structural similarity while effectively suppressing nonphysical artifacts in wind vector fields. Furthermore, by evaluating predictive uncertainty, the framework provides reliable probabilistic boundaries, offering a robust solution adhering to the intrinsic kinematic constraints of the atmosphere.

Yi Yan, Jiangting Li, Yuxuan Wang et al. · 0 citations