Precipitation Nowcasting over complex terrain in Greece, using weather radar and lighting data assimilation

Short-duration intense rainfall events drive some of the most destructive flood hazards in the Mediterranean, posing considerable difficulties for operational early warning systems. Reliable nowcasting (short-term forecasting) of convective rainfall is essential for hydrological response modelling and risk management. Nevertheless, numerical weather prediction models frequently fail to capture storm initiation and localization, especially over complex terrain.

The present study investigates the integration of polarimetric weather radar data into the Weather Research and Forecasting (WRF) model using a four-dimensional variation (4DVAR) data assimilation technique, to improve rainfall forecasts for flood-relevant time scales. Simulations are performed for selected high-impact precipitation events that occurred over Greece between 2024 and 2026, including cases associated with flash flooding. Through 4DVAR cycling, radar reflectivity and radial wind observations are assimilated, with simulations conducted at 1-km resolution and a 3-hour forecast horizon, aligned with nowcasting time scales. Additionally, humidity, vertical velocity and horizontal wind divergence profiles derived from lightning data at storm locations, are also assimilated with a three-dimensional variation (3DVAR) method. To assess whether data assimilation is sensitive to the choice of initial and boundary conditions, experiments with different initialization data (ICON and GFS) are performed. Results, using primarily the measured reflectivity and radial wind velocity from the weather radar and the proxy lightning data at larger range, indicate that assimilation using these data significantly improves convective initiation, storm structure, and peak rainfall placement during the first forecast hours. These findings suggest that radar-based 4DVAR assimilation has the potential to strengthen operational flood early-warning systems by delivering more reliable rainfall forcing hydrological and decision-support models. Ongoing studies examine its integration within multi-sensor workflows, coupling with meteorological forecasting chains, with the goal of operational implementation in Greece.