The Lagrangian particle dispersion model FLEXPART-WRF version 3.1

Brioude, J.; Arnold, Dèlia; Stohl, Andreas; Cassiani, Massimo; Morton, Don; Seibert, Petra; Angevine, W. M.; Evan, Stéphanie; Dingwell, Adam; Fast, J. D.; Easter, R. C.; Pisso, Ignacio; Burkhart, John F.; Wotawa, Gerhard

doi:10.5194/gmd-6-1889-2013

Cited by 320 publications

(284 citation statements)

References 47 publications

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“…For this study, we used 3-hourly ERA-Interim re-analysis data from ECMWF with a resolution of 1 • latitude × 1 • longitude and 61 vertical levels. There exist other versions of FLEX-PART, such as for the Weather Research and Forecasting (WRF) model (Brioude et al, 2013) or the Norwegian Earth System Model (NorESM)/Community Atmosphere Model (CAM) (Cassiani et al, 2016), in which our changes are not yet implemented. Of some importance for this paper is the fact that FLEXPART uses an internal terrain-following coordinate system that is automatically adjusted to the vertical resolution at which meteorological input data in native model coordinates are provided.…”

Section: Methodsmentioning

confidence: 99%

Source–receptor matrix calculation for deposited mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode

et al. 2017

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Abstract. Existing Lagrangian particle dispersion models are capable of establishing source-receptor relationships by running either forward or backward in time. For receptororiented studies such as interpretation of "point" measurement data, backward simulations can be computationally more efficient by several orders of magnitude. However, to date, the backward modelling capabilities have been limited to atmospheric concentrations or mixing ratios. In this paper, we extend the backward modelling technique to substances deposited at the Earth's surface by wet scavenging and dry deposition. This facilitates efficient calculation of emission sensitivities for deposition quantities at individual sites, which opens new application fields such as the comprehensive analysis of measured deposition quantities, or of deposition recorded in snow samples or ice cores. This could also include inverse modelling of emission sources based on such measurements. We have tested the new scheme as implemented in the Lagrangian particle dispersion model FLEXPART v10.2 by comparing results from forward and backward calculations. We also present an example application for black carbon concentrations recorded in Arctic snow.

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Section: Methodsmentioning

confidence: 99%

Source–receptor matrix calculation for deposited mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode

et al. 2017

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“…In addition, a version of the Lagrangian particle model, FLEXPART-WRF (FLEXible PARTicle dispersion model, Weather Research and Forecasting; Brioude et al, 2013), was used to estimate potential emission sensitivities. More details and figures of FLEXPART-WRF are published in other studies from the same campaign (NET-CARE 2014; e.g., Mungall et al, 2015;Wentworth et al, 2016).…”

Section: Field Description and Methodsmentioning

confidence: 99%

Biogenic, anthropogenic and sea salt sulfate size-segregated aerosols in the Arctic summer

et al. 2016

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Abstract. Size-segregated aerosol sulfate concentrations were measured on board the Canadian Coast Guard Ship (CCGS) Amundsen in the Arctic during July 2014. The objective of this study was to utilize the isotopic composition of sulfate to address the contribution of anthropogenic and biogenic sources of aerosols to the growth of the different aerosol size fractions in the Arctic atmosphere. Non-seasalt sulfate is divided into biogenic and anthropogenic sulfate using stable isotope apportionment techniques. A considerable amount of the average sulfate concentration in the fine aerosols with a diameter < 0.49 µm was from biogenic sources (> 63 %), which is higher than in previous Arctic studies measuring above the ocean during fall (< 15 %) (Rempillo et al., 2011) and total aerosol sulfate at higher latitudes at Alert in summer (> 30 %) (Norman et al., 1999). The anthropogenic sulfate concentration was less than that of biogenic sulfate, with potential sources being long-range transport and, more locally, the Amundsen's emissions. Despite attempts to minimize the influence of ship stack emissions, evidence from larger-sized particles demonstrates a contribution from local pollution.A comparison of δ 34 S values for SO 2 and fine aerosols was used to show that gas-to-particle conversion likely occurred during most sampling periods. δ 34 S values for SO 2 and fine aerosols were similar, suggesting the same source for SO 2 and aerosol sulfate, except for two samples with a relatively high anthropogenic fraction in particles < 0.49 µm in diameter (15)(16)(17). The high biogenic fraction of sulfate fine aerosol and similar isotope ratio values of these particles and SO 2 emphasize the role of marine organisms (e.g., phytoplankton, algae, bacteria) in the formation of fine particles above the Arctic Ocean during the productive summer months.

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“…The Git (https: //git-scm.com/) version control system is used. Most notably, a version adapted for the WRF (Weather Research and Forecasting) model has been documented extensively (Brioude et al, 2013). This version takes advantage of several different coordinate systems supported by WRF.…”

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The offline Lagrangian particle model FLEXPART–NorESM/CAM (v1): model description and comparisons with the online NorESM transport scheme and with the reference FLEXPART model

et al. 2016

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Abstract. The offline FLEXible PARTicle (FLEXPART) stochastic dispersion model is currently a community model used by many scientists. Here, an alternative FLEXPART model version has been developed and tailored to use with the meteorological output data generated by the CMIP5-version of the Norwegian Earth System Model (NorESM1-M). The atmospheric component of the NorESM1-M is based on the Community Atmosphere Model (CAM4); hence, this FLEXPART version could be widely applicable and it provides a new advanced tool to directly analyse and diagnose atmospheric transport properties of the state-of-theart climate model NorESM in a reliable way. The adaptation of FLEXPART to NorESM required new routines to read meteorological fields, new post-processing routines to obtain the vertical velocity in the FLEXPART coordinate system, and other changes. These are described in detail in this paper. To validate the model, several tests were performed that offered the possibility to investigate some aspects of offline global dispersion modelling. First, a comprehensive comparison was made between the tracer transport from several point sources around the globe calculated online by the transport scheme embedded in CAM4 and the FLEXPART model applied offline on output data. The comparison allowed investigating several aspects of the transport schemes including the approximation introduced by using an offline dispersion model with the need to transform the vertical coordinate system, the influence on the model results of the sub-grid-scale parameterisations of convection and boundary layer height and the possible advantage entailed in using a numerically non-diffusive Lagrangian particle solver. Subsequently, a comparison between the reference FLEX-PART model and the FLEXPART-NorESM/CAM version was performed to compare the well-mixed state of the atmosphere in a 1-year global simulation. The two model versions use different methods to obtain the vertical velocity but no significant difference in the results was found. However, for both model versions there was some degradation in the well-mixed state after 1 year of simulation with the buildup of a bias and an increased scatter. Finally, the capability of the new combined modelling system in producing realistic, backward-in-time transport statistics was evaluated calculating the average footprint over a 5-year period for several measurement locations and by comparing the results with those obtained with the reference FLEXPART model driven by re-analysis fields. This comparison confirmed the effectiveness of the combined modelling system FLEXPART with NorESM in producing realistic transport statistics.

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The Lagrangian particle dispersion model FLEXPART-WRF version 3.1

Cited by 320 publications

References 47 publications

Source–receptor matrix calculation for deposited mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode

Source–receptor matrix calculation for deposited mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode

Biogenic, anthropogenic and sea salt sulfate size-segregated aerosols in the Arctic summer

The offline Lagrangian particle model FLEXPART–NorESM/CAM (v1): model description and comparisons with the online NorESM transport scheme and with the reference FLEXPART model

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