Correlation of precipitable water vapor and heavy rainfall over Cyprus using GNSS sensors network - Giannadaki, Despina; Oikonomou, Christina; Haralambous, Haris (Frontiers in Signal Processing, 2025)
The application of technological systems to monitor and provide nowcasting, forecasting and early warning of convective storms, such as Medicanes (hurricane-like cyclonic systems in the Mediterranean Sea), particularly on a short-term temporal and small-scale spatial context, is crucial to a wide spectrum of societal sectors including public safety, protection of agricultural production and protection of infrastructures. Weather forecast updates in Numerical Weather Prediction (NWP) models suffer from two main problems: a) The updates on impending weather conditions, including alerts for precipitation, are issued every 6 h b) These updates may not represent the real weather conditions near the area of interest. Increasing the spatial and temporal coverage by meteorological radars can help to face these issues; however, it is a very costly solution, involving high initial purchase costs, installation expenses, and ongoing maintenance. Alternative low-cost solutions, such as GNSSs (Global Navigation Satellite Systems) are necessary to enhance the continuous atmospheric sensing of various parameters in near-real time including water vapor, temperature, and pressure, by analyzing the signals received from GNSS satellites. The rapid spatiotemporal variations of Precipitable Water Vapor (PWV) in the low atmosphere comprises one more challenge to NWP models forecasting accuracy. Though many studies have evidenced continuous reinforcement of PWV before the heavy rainfall, there is still a great difficulty to determine a rigid relationship between rainfall and PWV that could be incorporated to a nowcasting model. In this context, the present study aims to investigate the possible correlation between PWV and heavy rainfall, during 81 selected heavy and extreme precipitation events occurring during 2022-2024 over Cyprus Island. To achieve this, we exploited both GNSS and ERA5 (the fifth generation ECMWF atmospheric Reanalysis) PWV data and rainfall observations over 12 meteorological stations of Cyprus. An increase in PWV before most heavy rain events was found with the time-lag of PWV peaks from the heavy rain onset having a range from one to six hours in most events. The Correlation Coefficient R, between maximum PWV peaks and the related maximum precipitation peaks shows a very high correlation (R = 0.85) over the mountainous region of the island and a satisfactory correlation both in coastal and all Cyprus regions (R = 0.5).