HERMESv3, a stand-alone multi-scale atmospheric emission modelling framework – Part 1: global and regional module

Abstract Back to top We present the High-Elective Resolution Modelling Emission System version 3 (HERMESv3), an open source, parallel and stand-alone multi-scale atmospheric emission modelling framework that computes gaseous and aerosol emissions for use in atmospheric chemistry models. HERMESv3 is coded in Python and consists of a global_regional module and a bottom_up module that can be either combined or executed separately. In this contribution (Part 1) we describe the global_regional module, a customizable emission processing system that calculates emissions from different sources, regions and pollutants on a user-specified global or regional grid. The user can flexibly define combinations of existing up-to-date global and regional emission inventories and apply country-specific scaling factors and masks. Each emission inventory is individually processed using user-defined vertical, temporal and speciation profiles that allow obtaining emission outputs compatible with multiple chemical mechanisms (e.g. Carbon-Bond 05). The selection and combination of emission inventories and databases is done through detailed configuration files providing the user with a widely applicable framework for designing, choosing and adjusting the emission modelling experiment without modifying the HERMESv3 source code. The generated emission fields have been successfully tested in different atmospheric chemistry models (i.e. CMAQ, WRF-Chem and NMMB-MONARCH) at multiple spatial and temporal resolutions. In a companion article (Part 2; Guevara et al., 2019) we describe the bottom_up module, which estimates emissions at the source level (e.g. road link) combining state-of-the-art bottom–up methods with local activity and emission factors.The research leading to these results has received funding from the Ministerio de Economía y Competitividad (MINECO) as part of the PAISA project CGL2016-75725-R and the NUTRIENT project CGL2017-88911-R. The authors acknowledge PRACE for awarding access to Marenostrum4 based in Spain at the Barcelona Supercomputing Center through the Tier-0 HHRNTCP and Tier-0 EEDMC projects. Carlos Pérez García-Pando acknowledges long-term support from the AXA Research Fund, as well as the support received through the Ramón y Cajal programme (grant RYC-2015-18690) of the Spanish Ministry of Economy and Competitiveness. The authors would also like to thank the two anonymous referees for their thorough comments, which helped improve the quality of the paper.Peer Reviewe