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

Research output: Contribution to journal › Journal article › Research › peer-review

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A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event. / Foreback, Benjamin; Clusius, Petri; Mahura, Alexander; Xavier, Carlton; Baykara, Metin; Zhou, Putian; Nieminen, T.M.; Sinclair, Victoria; Kerminen, Veli-Matti; Kokkonen, T.; Hakala, Simo; Aliaga, Diego; Baklanov, Alexander; Nuterman, Roman; Xia, Men; Hua, Chenjie; Liu, Y.; Kulmala, Markku; Paasonen, Pauli; Boy, Michael.

In: Big Earth Data, 23.02.2024.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Foreback, B, Clusius, P, Mahura, A, Xavier, C, Baykara, M, Zhou, P, Nieminen, TM, Sinclair, V, Kerminen, V-M, Kokkonen, T, Hakala, S, Aliaga, D, Baklanov, A, Nuterman, R, Xia, M, Hua, C, Liu, Y, Kulmala, M, Paasonen, P & Boy, M 2024, 'A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event', Big Earth Data. https://doi.org/10.1080/20964471.2024.2316320

APA

Foreback, B., Clusius, P., Mahura, A., Xavier, C., Baykara, M., Zhou, P., Nieminen, T. M., Sinclair, V., Kerminen, V-M., Kokkonen, T., Hakala, S., Aliaga, D., Baklanov, A., Nuterman, R., Xia, M., Hua, C., Liu, Y., Kulmala, M., Paasonen, P., & Boy, M. (Accepted/In press). A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event. Big Earth Data. https://doi.org/10.1080/20964471.2024.2316320

Vancouver

Foreback B, Clusius P, Mahura A, Xavier C, Baykara M, Zhou P et al. A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event. Big Earth Data. 2024 Feb 23. https://doi.org/10.1080/20964471.2024.2316320

Author

Foreback, Benjamin ; Clusius, Petri ; Mahura, Alexander ; Xavier, Carlton ; Baykara, Metin ; Zhou, Putian ; Nieminen, T.M. ; Sinclair, Victoria ; Kerminen, Veli-Matti ; Kokkonen, T. ; Hakala, Simo ; Aliaga, Diego ; Baklanov, Alexander ; Nuterman, Roman ; Xia, Men ; Hua, Chenjie ; Liu, Y. ; Kulmala, Markku ; Paasonen, Pauli ; Boy, Michael. / A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event. In: Big Earth Data. 2024.

Bibtex

@article{1c8ede92f8904650b84265899d321aad,
title = "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.",
author = "Benjamin Foreback and Petri Clusius and Alexander Mahura and Carlton Xavier and Metin Baykara and Putian Zhou and T.M. Nieminen and Victoria Sinclair and Veli-Matti Kerminen and T. Kokkonen and Simo Hakala and Diego Aliaga and Alexander Baklanov and Roman Nuterman and Men Xia and Chenjie Hua and Y. Liu and Markku Kulmala and Pauli Paasonen and Michael Boy",
year = "2024",
month = feb,
day = "23",
doi = "10.1080/20964471.2024.2316320",
language = "English",
journal = "Big Earth Data",
issn = "2574-5417",
publisher = "Taylor & Francis",

}

RIS

TY - JOUR

T1 - A new implementation of FLEXPART with Enviro-HIRLAM meteorological input, and a case study during a heavy air pollution event

AU - Foreback, Benjamin

AU - Clusius, Petri

AU - Mahura, Alexander

AU - Xavier, Carlton

AU - Baykara, Metin

AU - Zhou, Putian

AU - Nieminen, T.M.

AU - Sinclair, Victoria

AU - Kerminen, Veli-Matti

AU - Kokkonen, T.

AU - Hakala, Simo

AU - Aliaga, Diego

AU - Baklanov, Alexander

AU - Nuterman, Roman

AU - Xia, Men

AU - Hua, Chenjie

AU - Liu, Y.

AU - Kulmala, Markku

AU - Paasonen, Pauli

AU - Boy, Michael

PY - 2024/2/23

Y1 - 2024/2/23

N2 - 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.

AB - 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.

U2 - 10.1080/20964471.2024.2316320

DO - 10.1080/20964471.2024.2316320

M3 - Journal article

JO - Big Earth Data

JF - Big Earth Data

SN - 2574-5417

ER -

ID: 383192404