Quality Control and Observation Thinning for 2-metre Temperature Assimilation in an Operational Convective Nowcasting Framework

This study investigates the impact of assimilating 2-metre temperature (T2M) observations into an operational convective-scale nowcasting system. The system is based on the WRF model at 2.5 km resolution and already includes the assimilation of radar reflectivity via 3DVAR and lightning observations through nudging techniques, delivering high-frequency forecasts to support real-time decision-making in civil protection.

The analysis focuses on August 2024 and uses T2M observations from both the Italian Civil Protection Department and the MeteoNetwork association. A unified and fully automatic quality control (QC) procedure is applied jointly to both datasets before assimilation. This procedure includes metadata validation (META), elevation consistency verification using a high-resolution digital elevation model, a background field check (BF) based on Optimal Interpolation, a spatial consistency test (SCT) using leave-one-out cross-validation, and a DZMIN check, which rejects stations whose elevation differs significantly from the model orography. Additionally, the standard WRFDA internal consistency check is performed to filter out observations too divergent from the short-range forecast background.

To align the observational dataset with the spatial resolution of the model and avoid redundancy, a thinning strategy is applied after QC to retain only a subset of spatially representative observations.

Two assimilation configurations are compared: one using all raw T2M observations without QC, and another using only QC-passed and thinned observations. Both are evaluated against the current operational baseline, which includes only radar and lightning assimilation. The comparison assesses differences in assimilation stability, consistency, and potential benefits derived from the integration of surface temperature data.

This work highlights the importance of structured quality control pipelines and data representativeness in high-resolution convective-scale data assimilation. It also confirms the potential of integrating dense observational networks—including non-institutional sources—into early warning frameworks. The results contribute to the objective of enhancing forecasting methodologies for extreme hydrometeorological events in support of environmental protection strategies across complex and vulnerable territories.

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.