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

Authors :
Benjamin Foreback
Alexander Mahura
Petri Clusius
Carlton Xavier
Metin Baykara
Putian Zhou
Tuomo Nieminen
Victoria Sinclair
Veli-Matti Kerminen
Tom V. Kokkonen
Simo Hakala
Diego Aliaga
Risto Makkonen
Alexander Baklanov
Roman Nuterman
Men Xia
Chenjie Hua
Yongchun Liu
Markku Kulmala
Pauli Paasonen
Michael Boy
Source :
Big Earth Data, Vol 8, Iss 2, Pp 397-434 (2024)
Publication Year :
2024
Publisher :
Taylor & Francis Group, 2024.

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.

Details

Language :
English
ISSN :
20964471 and 25745417
Volume :
8
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Big Earth Data
Publication Type :
Academic Journal
Accession number :
edsdoj.259bc68934e54e5a9e1078b9ff412ed0
Document Type :
article
Full Text :
https://doi.org/10.1080/20964471.2024.2316320