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FUME 2.0 – Flexible Universal processor for Modeling Emissions
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SYSNO ASEP 0585895 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title FUME 2.0 – Flexible Universal processor for Modeling Emissions Author(s) Belda, M. (CZ)
Benešová, N. (CZ)
Resler, Jaroslav (UIVT-O) SAI, RID, ORCID
Huszár, P. (CZ)
Vlček, O. (CZ)
Krč, Pavel (UIVT-O) SAI, RID, ORCID
Karlický, J. (CZ)
Juruš, Pavel (UIVT-O) SAI, RID
Eben, Kryštof (UIVT-O) SAI, RID, ORCIDSource Title Geoscientific Model Development. - : Copernicus GmbH - ISSN 1991-959X
Roč. 17, č. 9 (2024), s. 3867-3878Number of pages 12 s. Publication form Online - E Language eng - English Country DE - Germany Keywords Air quality modelling ; Emission modelling ; SMOKE ; emission inventories ; CTM OECD category Meteorology and atmospheric sciences R&D Projects TO01000219 GA TA ČR - Technology Agency of the Czech Republic (TA ČR) SS02030031 GA TA ČR - Technology Agency of the Czech Republic (TA ČR) Method of publishing Open access Institutional support UIVT-O - RVO:67985807 UT WOS 001222533900001 EID SCOPUS 85193542888 DOI https://doi.org/10.5194/gmd-17-3867-2024 Annotation 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. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2025 Electronic address https://doi.org/10.5194/gmd-17-3867-2024
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