Sparse and infrequent upper‑air observations over the Three‑River Source Region (TRSR) of the Tibetan Plateau (TP) severely limit the capability for extensive and continuous wind profile (WP) monitoring. A WP retrieval model was developed via ensemble learning (XGBoost+CatBoost), trained on TB data from 14 fixed-site ground-based microwave radiometers (GMWR) and WP radiosonde observations (Raob), and then applied to mobile GMWR observations over the TRSR. After strict QC of TB data, three distribution‑alignment methods (Quantile Transformer, PCA Whitening, RankGauss) were used to mitigate the TB discrepancy between fixed and mobile GMWR. A nonlinear weighted regression loss function was introduced to reduce the central tendency effect in long‑tailed wind‑speed distributions. Retrieved WP from 14 fixed‑site GMWR test data agreed well with Raob (R = 0.96, RMSE = 2.24 m s^−1, MAE = 1.57 m s^−1). Retrieval errors in the 600–700 hPa layer were lower at 08:00 BT than at 20:00, and smaller in lower‑altitude regions. Mobile GMWR retrievals reproduced WP variability with mean biases below 2 m s^−1 relative to Raob, though errors increased for upper‑level strong winds and at high altitudes; against Raob‑corrected ERA5, the RMSE and MAE were 2.66 and 2.25 m s^−1, respectively. This study offers a novel pathway to optimize ground‑based vertical remote sensing, enhance meteorological support for the low‑altitude economy, and improve wind energy assessment and forecasting.