A generalized user-friendly method for fusing observational data and chemical transport model (Gen-Friberg V1.0: GF-1)

A generalized, user-friendly data fusion method (Gen-Friberg) to reduce differences between chemical transport models (CTMs) and observational data is implemented to be compatible with widely used CTMs such as CMAQ, GEOS-Chem, and WRF-Chem. Key source code improvements included encapsulating the data fusion algorithm within a single function and enabling parallel processing to minimize runtime for long simulations. We applied the data fusion method to CMAQ outputs and observations from 2010 to 2019 to evaluate the method's performance. After data fusion, pollutant concentration fields showed improved performance. Additionally, we assessed the generalizability of the data fusion method by demonstrating its effectiveness in reducing bias in the GEOS-Chem and WRF-Chem concentration fields using evaluations based on 2017 simulations. Comparisons across CMAQ, GEOS-Chem, and WRF-Chem with and without data fusion demonstrate that data fusion reduces inter-model discrepancies, yielding more consistent concentration fields for use in health and policy assessments. • A generalized user-friendly data fusion method is developed to reduce CTM bias. • Method effectiveness is evaluated for CMAQ, GEOS-Chem, and WRF-Chem. • The data fusion method reduces inconsistencies among different CTMs. • The method is open-sourced and adaptable for air-quality and health assessments.