FUME 2.0 – Flexible Universal processor for Modeling Emissions
Abstract. This manuscript introduces FUME 2.0, an open-source emission processor for air quality modeling, documenting 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 it 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, WRF-Chem, and 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.
Status: open (until 27 Feb 2024)
FUME user cases https://doi.org/10.48700/datst.bf6s2-5tq48
Model code and software
FUME Official 2.0 release https://doi.org/10.5281/zenodo.10142912
Viewed (geographical distribution)