Enhancing GNSS Water Vapor Retrieval via Synergistic Microwave Radiometry: Thermodynamic Error Diagnosis and Bias Correction

Abstract. The retrieval of Precipitable Water Vapor (PWV) from Global Navigation Satellite Systems (GNSS) in thermodynamically complex environments is fundamentally limited by the accuracy of the weighted mean temperature (T_m). This study evaluates the efficacy of static climatological models versus dynamic ground-based microwave radiometry for T_m determination in the Eastern Mediterranean, a region characterized by sharp refractivity gradients. Using the Cyprus GNSS Meteorology Enhancement (CYGMEN) infrastructure in Nicosia, the performance of the ERA5-based HGPT2 model and a co-located Microwave Radiometer (MWR) was assessed against radiosonde (RS) profiles during the 2025 warm season (Spring–Summer). Diagnostic analysis reveals that the static HGPT2 model fails to resolve the diurnal thermodynamic decoupling between the boundary layer and the free troposphere, leading to a systematic overestimation of T_m exceeding 6 K during peak solar insolation. Conversely, the MWR captures short-term thermodynamic variability (r=0.98) but exhibits a systematic cold bias of −1.91 K in raw retrievals. It is demonstrated that a site-specific linear bias correction reduces the MWR T_m Root Mean Square Error (RMSE) from 2.32 K to 1.43 K, significantly outperforming the empirical model. Sensitivity analysis confirms that thermodynamic uncertainty dominates the error budget, outweighing uncertainties in refractivity constants by an order of magnitude. Consequently, standard climatological retrievals diverge from the synergistic MWR-GNSS method during extreme hygrometric events, introducing systematic PWV biases exceeding 1.0 mm when moisture levels surpass 45 mm. The synergistic coupling of real-time radiometric T_m with GNSS data is therefore essential for generating climate-quality PWV records in semi-arid coastal regions.