Improving GNSS Water Vapor Monitoring in Cyprus climate change hotspot Using MWR-Derived Tm

The Eastern Mediterranean is a recognized climate-change hotspot, characterized by strong summertime subsidence, sharp land–sea moisture gradients, and frequent thermodynamic extremes. Although Global Navigation Satellite System (GNSS) observations provide continuous and all-weather monitoring of precipitable water vapor (PWV), their accuracy critically depend on the weighted mean atmospheric temperature (T_m) used to convert zenith total delay (ZTD) into water vapor content. This study presents the first comprehensive analysis of radiometric data acquired under the Cyprus GNSS Meteorology (CYGMEN) strategic infrastructure project, established to monitor the thermodynamic state of the Eastern Mediterranean atmosphere. This study quantifies the impact of T_m uncertainty on GNSS-PWV retrievals and assesses the benefit of ground-based microwave radiometer (MWR) observations under extreme thermodynamic conditions.

MWR- and GNSS-derived products are evaluated against Vaisala RS41 radiosonde observations at Nicosia, Cyprus, for the period March–October 2025. Baseline validation demonstrates that the MWR provides highly accurate temperature profiling in the boundary layer (correlation coefficient r > 0.98) and reliable integrated water vapor estimates, with an RMSE of 1.72 kg m⁻² relative to radiosondes. However, the MWR exhibits limited skill in resolving vertical humidity structure, as indicated by a negative coefficient of determination (R² = −2.87) for moisture scale-height comparisons. This highlights that the primary strength of the MWR lies in constraining the column-integrated thermodynamic state rather than detailed vertical moisture profiling.

Incorporation of MWR-derived T_m into the GNSS processing chain substantially improves PWV retrievals during periods of strong thermodynamic variability, particularly under high-PWV and subsidence-dominated conditions typical of the Eastern Mediterranean summer. The proposed GNSS–MWR synergistic framework provides a physically consistent pathway to reduce T_m-related uncertainties and enhance GNSS-PWV reliability in climate-sensitive regions.