A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event

ABSTRACT

We integrated Enviro-HIRLAM (Environment-High Resolution Limited Area Model) meteorological output into FLEXPART (FLEXible PARTicle dispersion model). A FLEXPART simulation requires meteorological input from a numerical weather prediction (NWP) model. The publicly available version of FLEXPART can utilize either ECMWF (European Centre for Medium-range Weather Forecasts) Integrated Forecast System (IFS) forecast or reanalysis NWP data, or NCEP (U.S. National Center for Environmental Prediction) Global Forecast System (GFS) forecast or reanalysis NWP data. The primary benefits of using Enviro-HIRLAM are that it runs at a higher resolution and accounts for aerosol effects in meteorological fields. We compared backward trajectories generated with FLEXPART using Enviro-HIRLAM (both with and without aerosol effects) to trajectories generated using NCEP GFS and ECMWF IFS meteorological inputs, for a case study of a heavy haze event which occurred in Beijing, China in November 2018. We found that results from FLEXPART were considerably different when using different meteorological inputs. When aerosol effects were included in the NWP, there was a small but noticeable difference in calculated trajectories. Moreover, when looking at potential emission sensitivity instead of simply expressing trajectories as lines, additional information, which may have been missed when looking only at trajectories as lines, can be inferred.

KEYWORDS:

• Atmospheric and chemical transport modelling
• trajectory and particle dispersion modelling
• severe air pollution episode
• FLEXPART
• Enviro-HIRLAM

Acknowledgements

We wish to acknowledge CSC – IT Center for Science, Finland (www.csc.fi/csc), for computational resources, technical support and advice used in this project.

We also wish to acknowledge the FLEXPART development team (FlexTeam, Univ. of. Vienna; flexteam.univie.ac.at), especially Andreas Plach and Lucie Bakels, for technical support, advice, and assistance on setting up and running FLEXPART.

CSC (Atos BullSequana) HPC & ECMWF (CRAY-XC) HPCs were used. The ECMWF boundary conditions, meteorological and air quality observations/datasets are utilized in this project.

The work is supported by Enviro-PEEX(Plus) on ECMWF (www.atm.helsinki.fi/peex/index.php/enviro-peex_plus), Enviro-HIRLAM on CSC-HPC projects, AoF ACCC, H2020 CRiceS.

Additionally, we acknowledge Prof. Jim Steenburgh, with whom we had a brief conversation about the meteorological conditions in Beijing, and he provided insight into the synoptic meteorology timeline during the case study period.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

1. New FLEXPART code

The FLEXPART code used for this publication has been uploaded to Zenodo, available at https://doi.org/10.5281/zenodo.8300429. The code will also be available in a repository on the FLEXPART website at https://doi.org/10.5281/zenodo.8300429, and new versions will be published here. We suggest potential users who wish to use the Enviro-HIRLAM-FLEXPART modelling system use the version that will be published on the FLEXPART website. We also plan to work with the FLEXPART team to integrate this into a future version of the public FLEXPART version. We suggest that users refer to the official FLEXPART website for the latest version.

2. Enviro-HIRLAM post-processing script

An example script to select and merge the Enviro-HIRLAM output for use in FLEXPART is in Appendix C of this manuscript.

3. Enviro-HIRLAM model code

The Enviro-HIRLAM modelling system is a community model. The source code is available for non-commercial use (i.e. research, development and science education) upon agreement through contact with Alexander Mahura ([email protected]), Bent Sass ([email protected]) and Roman Nuterman ([email protected]). Documentation, educational materials and practical exercises are available from http://hirlam.org and hirlam.org/index.php/documentation/chemistry-branch, and Young Scientist Schools (netfam.fmi.fi/YSSS08, www.ysss.osenu.org.ua; aveirosummerschool2014.web.ua.pt and https://megapolis2021.ru).

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/20964471.2024.2316320

Additional information

Funding

Funding for this project came in part from the Jenny and Antti Wihuri Foundation project, with the grant for “Air pollution cocktail in Gigacity. ”Funding was also received from the Research Council of Finland (formerly the Academy of Finland, AoF) project 311932 and applied towards this project. Partially, funding included contribution from EU Horizon 2020 CRiceS project “Climate relevant interactions and feedbacks: the key role of sea ice and snow in the polar and global climate system” under grant agreement No 101003826; and AoF project ACCC “The Atmosphere and Climate Competence Center” under grant agreement No 337549. Additionally, internal funding at the University of Helsinki’s Lahti University Campus was used for work on this project.

Notes on contributors

Benjamin Foreback

Benjamin Foreback is a PhD student in atmospheric science at the Lahti campus of the University of Helsinki. He has bachelor’s degrees in physics and atmospheric science from the University of Utah and a master’s degree in atmospheric science from the University of Helsinki. His research includes modelling and observations of air quality, with a focus on severe air pollution episodes in megacities. His anticipated graduation is early spring 2024.

Alexander Mahura

Alexander Mahura is University Researcher at Institute for Atmospheric and Earth System Research, University of Helsinki (UH), Finland since 2017; received BSc in 1991, MSc in 1998, and PhD Phys&Math Atm.Sci. in 2002; worked in Russia, USA, Austria, France, Denmark, and Finland. His research interests include online integrated multi-scales and -processes modelling of meteorology and atmospheric composition, atmospheric chemistry, atmospheric boundary layer processes, numerical weather prediction, fine-scale road weather and birch pollen forecasting, statistics for data analysis and post-processing models output, environmental impact and risk assessment.

Petri Clusius

Petri Clusius has worked with atmospheric modelling since his bachelor studies in 2017, and has focused heavily on new particle formation in boreal forests. His master thesis work was the development of the new Atmospherically Relevant Chemistry and Aerosol Box Model – ARCA box. In his PhD studies he is now focusing on the contributing role of biogenic hydrocarbons emitted from forests to aerosol concentrations and their effect to cloud condensation nuclei number concentration. In this work he is using the ARCA-model together with its 1D version SOSAA and developing it to a Lagrangian trajectory model.

Carlton Xavier

Carlton Xavier is a post-doctoral researcher at Lund university and SMHI who’s work focuses primarily on the formation and growth if secondary aerosols in polar regions. To accomplish this, he uses a detailed chemical and aerosol column model ADCHEM which is run along Lagrangian trajectories generated using Flexpart. The overarching aim of his research is to understand the radiative effects of these marine aerosols on polar climate.

Metin Baykara

Metin Baykara is a researcher at the Istanbul Technical University, Eurasia Institute of Earth Sciences, Department of Climate and Marine Sciences. He is also part of the University of Helsinki, Institute for Atmospheric and Earth System Research, Multiscale Modeling group.

Michael Boy

Michael Boy is a Professor of Atmospheric Science and Applied Mathematics. The position is shared between the University of Helsinki, INAR and the Lappeenranta University of Technology, Department of Computational Engineering. His main scientific interests are in atmospheric chemistry and aerosol dynamics and how to apply machine learning and artificial intelligence techniques in these areas.