Bridging ERA5 Reanalysis Data and Regulatory Air Dispersion Modelling: A Transparent Workflow Implemented through the WindRose Toolkit

Paolo Bidello

Regulatory atmospheric dispersion models such as AERMOD and CALPUFF are widely adopted for Environmental Impact Assessments involving atmospheric emissions. Despite their scientific maturity and regulatory acceptance, the practical application of these modelling systems outside the North American context is often constrained by the limited availability of suitable meteorological datasets. In many regions, the absence of long-term, spatially representative atmospheric observations represents a significant barrier to the routine implementation of regulatory dispersion modelling workflows.
At the same time, global atmospheric reanalysis datasets such as ERA5 (Hersbach et al., 2020), produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), provide an increasingly reliable and globally accessible source of meteorological information. However, the integration of ERA5 datasets into regulatory dispersion modelling remains technically challenging due to differences between the gridded structure of reanalysis data and the specific input formats required by meteorological preprocessors used in regulatory modelling systems.
This work presents a transparent workflow designed to bridge ERA5 atmospheric reanalysis datasets and regulatory dispersion modelling frameworks. The proposed methodology focuses on the transformation of ERA5 meteorological data into structured time series compatible with regulatory preprocessing systems such as AERMET and CALMET.
The workflow is implemented through a lightweight software toolkit, implemented in the WindRose Studio software environment, which enables the ingestion, inspection and transformation of ERA5 GRIB datasets into meteorological time series suitable for dispersion modelling applications. The proposed approach is illustrated through a real-world application example involving the mechanical-biological treatment facility located in Battipaglia (Southern Italy), demonstrating the full sequence from ERA5 dataset request planning to exploratory statistical characterization of reconstructed wind regimes.
The results highlight the potential of ERA5 reanalysis data as a robust and globally accessible meteorological data source for regulatory dispersion modelling, particularly in regions where observational datasets are sparse or incomplete. By providing a transparent and reproducible pathway from ERA5 datasets to regulatory-ready meteorological inputs, the methodology presented in this work contributes to bridging the gap between modern atmospheric reanalysis products and established regulatory air-quality modelling frameworks.