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
Documents
- A new implementation of FLEXPART with Enviro-HIRLAM meteorological input and a case study during a heavy air pollution event
Final published version, 22.2 MB, PDF document
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.
Original language | English |
---|---|
Journal | Big Earth Data |
Volume | 8 |
Issue number | 2 |
Pages (from-to) | 397-434 |
Number of pages | 38 |
ISSN | 2574-5417 |
DOIs | |
Publication status | Published - 23 Feb 2024 |
ID: 383192404