Impact of CyMETEO Data Assimilation on Cold-Season Precipitation Forecasts over Cyprus

Accurate forecasting of cold season precipitation in the Eastern Mediterranean, particularly over the complex terrain of Cyprus Island, remains a persistent operational challenge. In a novel study for this region, we evaluate the impact of assimilating high-resolution, state-of-the-art remote sensing observations from the CYGMEN project (Cyprus GNSS Meteorology Enhancement) infrastructure (CyMETEO) on short-range numerical weather prediction.

Using the Weather Research and Forecasting (WRF) model configured at a convection-permitting resolution of 2 km, we employ the WRFDA 3D-Var system to assimilate dense, localized observation networks. The assimilated datasets comprise Zenith Total Delay (ZTD) from a regional GNSS network, kinematic profiles from a Wind Profiler Radar, and vertical temperature and relative humidity profiles from a Microwave Radiometer (MWR), alongside conventional meteorological data from the Global Telecommunication System (GTS). Prior to assimilation, all non-conventional observations underwent a rigorous Quality Assurance and Quality Control (QA/QC) protocol via intercomparison with reference radiosonde profiles. Crucially, the standard deviations derived from this validation were explicitly utilized to define the observational error covariances within the DA framework, ensuring an optimal weighting of the ingested data. To quantify the added value of these advanced observations, a full cyclic assimilation suite was contrasted against an open-loop control run across four distinct heavy precipitation events during the 2025–2026 cold season. Model performance was systematically evaluated against an independent network of Automatic Weather Stations (AWS). Objective verification demonstrates a substantial improvement in Quantitative Precipitation Forecasts (QPF), notably reflected in higher Fractional Skill Scores (FSS) and a reduced false alarm ratio at higher rainfall thresholds. The data assimilation cycling successfully mitigated the severe overestimation of precipitation prevalent in the control experiments by correcting a pervasive low-level moist bias. Specifically, the assimilation of GNSS-ZTD effectively constrained the Integrated Water Vapor (IWV) field, leading to a much sharper spatial localization of orographic rain bands over the terrain. Furthermore, the combined ingestion of MWR thermodynamic profiles and wind profiler kinematics optimized the Convective Available Potential Energy (CAPE) and improved the representation of low-level wind shear. This resulted in a far more accurate depiction of boundary layer moisture convergence and the precise temporal initiation of convection. These findings highlight the critical value of the CYGMEN network in improving regional numerical weather prediction over the Cyprus Island.