0585895 - ÚI 2025 RIV DE eng J - Článek v odborném periodiku
Belda, M. - Benešová, N. - Resler, Jaroslav - Huszár, P. - Vlček, O. - Krč, Pavel - Karlický, J. - Juruš, Pavel - Eben, Kryštof
FUME 2.0 – Flexible Universal processor for Modeling Emissions.
Geoscientific Model Development. Roč. 17, č. 9 (2024), s. 3867-3878. ISSN 1991-959X. E-ISSN 1991-9603
Grant CEP: GA TA ČR(CZ) TO01000219; GA TA ČR(CZ) SS02030031
Grant ostatní: TA ČR(CZ) TA04020797
Institucionální podpora: RVO:67985807
Klíčová slova: Air quality modelling * Emission modelling * SMOKE * emission inventories * CTM
Obor OECD: Meteorology and atmospheric sciences
Impakt faktor: 4, rok: 2023 ; AIS: 2.327, rok: 2023
Způsob publikování: Open access
Web výsledku:
https://doi.org/10.5194/gmd-17-3867-2024DOI: https://doi.org/10.5194/gmd-17-3867-2024

This paper introduces FUME 2.0, an open-source emission processor for air quality modeling, and documents the software structure, capabilities, and sample usage. FUME provides a customizable framework for emission preparation tailored to user needs. It is designed to work with heterogeneous emission inventory data, unify them into a common structure, and generate model-ready emissions for various chemical transport models (CTMs). Key features include flexibility in input data formats, support for spatial and temporal disaggregation, chemical speciation, and integration of external models like MEGAN. FUME employs a modular Python interface and PostgreSQL/PostGIS backend for efficient data handling. The workflow comprises data import, geographical transformation, chemical and temporal disaggregation, and output generation steps. Outputs for mesoscale CTMs CMAQ, CAMx, and WRF-Chem and the large-eddy-simulation model PALM are implemented along with a generic NetCDF format. Benchmark runs are discussed on a typical configuration with cascading domains, with import and preprocessing times scaling near-linearly with grid size. FUME facilitates air quality modeling from continental to regional and urban scales by enabling effective processing of diverse inventory datasets.
Trvalý link: https://hdl.handle.net/11104/0353539


Vědecká data: Supplement at Publisher´s website