SWING: A Post-Processing Algorithm for Improved Nowcasting and Environmental Safeguard

This work presents SWING (Score-Weighted Improved NowcastinG), a novel post-processing algorithm designed to improve the accuracy and reliability of very short-term rainfall forecasts (nowcasting). SWING enhances the spatial and temporal predictability of convective rainfall events by combining high-resolution numerical weather prediction (NWP) outputs from the WRF model with radar-based nowcasting from the PhaSt system.

The algorithm operates by merging three forecasts over a 6-hour time window, updated every three hours, and weighing them based on recent performance. This evaluation is performed through an object-based comparison of modelled and observed rainfall fields using merged radar and rain gauge data. Each forecast is assigned a Reliability Score (RS) derived from spatial overlap, rainfall intensity, and object morphology, ensuring the final blended forecast maximizes accuracy while minimizing false alarms.

SWING has been running continuously for over a year, integrating high-resolution forecasts from the WRF model—updated every three hours using 3DVAR radar reflectivity assimilation and lightning data nudging—with radar-based nowcasting from the PhaSt system through a blending technique. A seasonal-scale validation of SWING against the standalone deterministic model run has been continuously performed since the system became operational.

SWING is fully automated and capable of generating rainfall scenarios and impact-based warnings through the output of a hydrological model (Continuum). Its rapid update cycle (3-hourly) makes it particularly suitable for operational early warning contexts where expert manual intervention is not feasible.

To assess its versatility, SWING is being extended within a multi-model forecasting framework. Preliminary tests will be presented to assess the algorithm’s ability to ingest and process outputs from different models, opening the door to ensemble-based rainfall scenarios and more robust hazard forecasting.

This research is conducted within the PNRR RAISE initiative - Spoke 3, which develops innovative technologies for environmental safeguard in water, air, and soil domains over the Ligurian region.