Abstract

<p>Following its release and corresponding publication in GMD, we present the Lagrangian model FLEXPART 10.4, which simulates the transport, diffusion, dry and wet deposition, radioactive decay and first order chemical reactions of atmospheric tracers. The model has been recently updated, both technical and in the representation of physico-chemical processes.<span> </span></p><p>FLEXPART was in its original version in the mid-1990s designed for calculating the long-range and mesoscale dispersion of hazardous substances from point sources, such as released after an accident in a nuclear power plant. Given suitable meteorological input data, it can be used for scales from dozens of meters to the global scale. In particular, inverse modelling based on source-receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10.4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts’ (ECMWF) Integrated Forecast System (IFS), and data from the United States’ National Centers of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEXPART description (version 6.2), the model has been improved in different aspects such as performance, physico-chemical parametrizations, input/output formats and available pre- and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75% for up to 64 processes on multiple nodes in runs with very large numbers of particles. The deviation from 100% efficiency is almost entirely due to remaining non-parallelized parts of the code, suggesting large potential for further speed-up. A new turbulence scheme for the convective boundary layer has been developed that considers the skewness in the vertical velocity distribution (updrafts and downdrafts) and vertical gradients in air density. FLEXPART is the only model available considering both effects, making it highly accurate for small-scale applications, e.g. to quantify dispersion in the vicinity of a point source. The wet deposition scheme for aerosols has been completely rewritten and a new, more detailed gravitational settling parameterization for aerosols has also been implemented. FLEXPART has had the option for running backward in time from atmospheric concentrations at receptor locations for many years, but this has now been extended to work also for deposition values . To our knowledge, to date FLEXPART is the only model with that capability. Furthermore, temporal variation and temperature dependence of chemical reactions with the OH radical have been included, allowing more accurate simulations for species with intermediate lifetimes against the reaction with OH, such as ethane. Finally, user settings can now be specified in a more flexible namelist format, and output files can be produced in NetCDF format instead of FLEXPART’s customary binary format. In this paper, we describe these new developments. Moreover, we present some<span> </span> tools for the preparation of the meteorological input data and for processing of FLEXPART output data and briefly report on alternative FLEXPART versions.<span> </span></p>

Highlights

  • Multi-scale offline Lagrangian particle dispersion models (LPDMs) are versatile tools for simulating the transport and turbulent mixing of gases and aerosols in the atmosphere

  • We describe FLEXPART developments since Stohl et al (2005), which led to the current version 10.4

  • Other FLEXPART model branches have been developed for input data from various limited-area models, for example the Weather Research and Forecasting (WRF) meteorological model (Brioude et al, 2013) and the Consortium for Small-scale Modeling (COSMO) model (Oney, 2015), which extend the applicability of FLEXPART down to the mesogamma scale

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Summary

Introduction

Multi-scale offline Lagrangian particle dispersion models (LPDMs) are versatile tools for simulating the transport and turbulent mixing of gases and aerosols in the atmosphere. An important characteristic of LPDMs is their ability to run backward in time in a framework that is theoretically consistent with both the Eulerian flow field and LPDM forward calculations This was discussed by Thomson (1987, 1990), further developed by Flesch et al (1995), and extended to global-scale dispersion by Stohl et al (2003) and Seibert and Frank (2004). In Eulerian models a tracer released from a point source is instantaneously mixed within a grid box, whereas Lagrangian models are independent of a computational grid and can account for point or line sources with potentially infinitesimal spatial resolution When combined with their capability to run backward in time, this means that LPDMs can be used to investigate the history of air parcels affecting, for instance, an atmospheric measurement site (e.g., for in situ monitoring of atmospheric composition). Methods should be used to reduce the statistical error (e.g., Heinz et al, 2003), such as kernels or particle splitting, and it is important to quantify the statistical error

The Lagrangian particle dispersion model FLEXPART
FLEXPART and its history
Updates of the model physics and chemistry
Boundary layer turbulence
Wet deposition
Definition of clouds, cloud water content and precipitation
Below-cloud scavenging
In-cloud scavenging
Influence of wet scavenging on the aerosol lifetime
Source–receptor matrix calculation of deposited mass backward in time
Sensitivity to initial conditions
Chemical reactions with the hydroxyl radical (OH)
Dust mobilization scheme
Parallelization
Implementation
Performance aspects
Validation
Required libraries and FLEXPART download
Compiling and running the serial version
Compiling and running the parallel version
FLEXPART input
Run-defining settings: the options directory
File COMMAND
File RELEASES
SPECIES files
File OUTGRID
File OUTGRID_NEST
File AGECLASSES
File RECEPTORS
Static data input files
Meteorological data and preprocessing routines
ECMWF data retrieval
NCEP data retrieval
Output files overview
Sparse matrix output
NetCDF output
Post-processing routines
Application examples
System requirements
Installing GRIB libraries
Installing NetCDF libraries
Installing FLEXPART
Meteorological input for the examples
Running the default example: installation verification
Generating variations of the default example
Running the examples
Comparing the results
FLEXPART–COSMO
FLEXPART–AROME
Findings
TRACZILLA
Full Text
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