Accurate estimation of the weighted mean temperature (Tm) is essential for retrieving precipitable water vapor (PWV) from ground-based Global Navigation Satellite System (GNSS) observations. Machine learning (ML) techniques excel in modeling nonlinear relationships among Tm time series, station geographic coordinates, and surface meteorological parameters, and recent studies have demonstrated that ML and neural network models outperform conventional linear Tm models. However, the full potential of surface meteorological measurements at GNSS stations for high-precision Tm retrieval remains to be fully explored. This study develops regional Tm empirical models using two ML methods—random forest (RF) and Temporal Mixture of Experts with Sequential Attention (TMESA)—to generate reliable real-time Tm estimates and enhance the accuracy of operational GNSS-PWV retrievals over China. A traditional linear model is adopted as the baseline to evaluate the performance improvements of the proposed models. The models are trained and tested using 10-year (2014–2023) hourly ERA5-derived Tm products and in-situ surface pressure, temperature, and relative humidity from 2377 meteorological stations, with Tm diurnal variations, station coordinates, and day of year integrated as auxiliary predictive features. Validation is conducted using 2024 ERA5 reanalysis data and radiosonde profiles from 120 stations across China. Results show that the RF model yields a bias (RMSE) of −0.11 K (2.67 K) against ERA5 and −0.21 K (2.67 K) against radiosonde data, while the TMESA model achieves superior performance with bias (RMSE) of −0.02 K (2.34 K) and 0.09 K (2.46 K), respectively, whose performance levels comparable to state-of-the-art studies. Compared with the traditional linear model, the RF model reduces Tm RMSE by 32% against ERA5 and 25% against radiosonde data, while the TMESA model achieves reductions of 40% and 33%, respectively. These findings confirm that the proposed ML models can provide high-accuracy Tm estimates for reliable GNSS-PWV retrieval. Future work will focus on the operational application of these models for near-real-time GNSS-PWV estimation.

With the rapid exploitation of offshore wind energy in the Beibu Gulf (BG), understanding local low-level wind variability is essential for wind farm operations. This study examines the interannual relationships between the BG low-level winds in June and tropical sea surface temperature (SST) during 1993–2021 using multiple datasets. The meridional and zonal winds show negligible correlation on interannual time scales. Further analysis indicates that the meridional wind over the BG is significantly linked to the tropical Indian Ocean (TIO) and tropical Atlantic (TA) SST. The TIO warming is able to intensify the Western Pacific Subtropical High via eastward-propagating Kelvin waves, inducing southerly wind anomalies over the BG. In contrast, the TA warming modulates the Walker circulation and triggers westward-propagating Rossby wave trains, forming an anomalous Philippine anticyclone and associated southerly winds. The anomalous southerly winds associated with TIO (TA) warming are contributed by changes in both rotational and divergent wind components (primarily divergent wind component). Conversely, the zonal wind over the BG is significantly correlated with the tropical Pacific SST. The equatorial eastern Pacific warming excites westward-propagating Rossby waves, generating an anomalous anticyclone and resulting in westerly anomalies over the BG. Air–sea coupling links warm SST in the northwestern Pacific to a local anticyclonic circulation, forming easterly anomalies in the BG. Notably, the tropical SST associated zonal wind anomalies are primarily driven by rotational wind component. This study clarifies how tropical SST anomalies influence low-level winds over the Beibu Gulf and distinguishes the roles of rotational and divergent wind components, providing new insights into the predictability of local wind variability.