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

2017 ◽  
Vol 10 (12) ◽  
pp. 4605-4618 ◽  
Author(s):  
Sabine Eckhardt ◽  
Massimo Cassiani ◽  
Nikolaos Evangeliou ◽  
Espen Sollum ◽  
Ignacio Pisso ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Measurement Data ◽  
Ice Cores ◽  
Particle Dispersion ◽  
Lagrangian Particle ◽  
Mixing Ratios ◽  
Efficient Calculation ◽  
Wet Scavenging ◽  
Application Fields ◽  
Atmospheric Concentrations

Abstract. Existing Lagrangian particle dispersion models are capable of establishing source–receptor relationships by running either forward or backward in time. For receptor-oriented 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.

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

2017 ◽  
Author(s):  
Sabine Eckhardt ◽  
Massimo Cassiani ◽  
Nikolaos Evangeliou ◽  
Espen Sollum ◽  
Ignacio Pisso ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Ice Cores ◽  
Particle Dispersion ◽  
Lagrangian Particle ◽  
Mixing Ratios ◽  
Efficient Calculation ◽  
Wet Scavenging ◽  
Application Fields ◽  
Atmospheric Concentrations ◽  
Matrix Calculation

Abstract. Existing Lagrangian particle dispersion models are capable of establishing source-receptor relationships by running either forward or backward in time. For many applications, 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, 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.

scholarly journals Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2

2005 ◽  
Vol 5 (9) ◽  
pp. 2461-2474 ◽  
Author(s):  
A. Stohl ◽  
C. Forster ◽  
A. Frank ◽  
P. Seibert ◽  
G. Wotawa
Keyword(s):  
Nuclear Power ◽  
Water Cycle ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Technical Note ◽  
Point Sources ◽  
Lagrangian Particle ◽  
Modeling And Analysis ◽  
Application Fields

Abstract. The Lagrangian particle dispersion model FLEXPART was originally (about 8 years ago) designed for calculating the long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis. Its application fields were extended from air pollution studies to other topics where atmospheric transport plays a role (e.g., exchange between the stratosphere and troposphere, or the global water cycle). It has evolved into a true community model that is now being used by at least 25 groups from 14 different countries and is seeing both operational and research applications. A user manual has been kept actual over the years and was distributed over an internet page along with the model's source code. In this note we provide a citeable technical description of FLEXPART's latest version (6.2).

scholarly journals Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2

2005 ◽  
Vol 5 (4) ◽  
pp. 4739-4799 ◽  
Author(s):  
A. Stohl ◽  
C. Forster ◽  
A. Frank ◽  
P. Seibert ◽  
G. Wotawa
Keyword(s):  
Nuclear Power ◽  
Water Cycle ◽  
Atmospheric Transport ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Technical Note ◽  
Point Sources ◽  
Lagrangian Particle ◽  
Modeling And Analysis ◽  
Application Fields

Abstract. The Lagrangian particle dispersion model FLEXPART was originally (about 8 years ago) designed for calculating the long-range and mesoscale dispersion of air pollutants from point sources, such as after an accident in a nuclear power plant. In the meantime FLEXPART has evolved into a comprehensive tool for atmospheric transport modeling and analysis. Its application fields were extended from air pollution studies to other topics where atmospheric transport plays a role (e.g., exchange between the stratosphere and troposphere, or the global water cycle). It has evolved into a true community model that is now being used by at least 25 groups from 14 different countries and is seeing both operational and research applications. A user manual has been kept actual over the years and was distributed over an internet page along with the model's source code. However, so far there was no citeable description of FLEXPART. In this note we provide a description of FLEXPART's latest version (6.2).

Inverse modeling of halocarbons: sensitivity to the baseline definition

2021 ◽  
Author(s):  
Martin Vojta ◽  
Rona Thompson ◽  
Christine Groot Zwaaftink ◽  
Andreas Stohl
Keyword(s):  
Atmospheric Chemistry ◽  
Inverse Modeling ◽  
Sulfur Hexafluoride ◽  
Dispersion Model ◽  
Particle Dispersion ◽  
Surface Fluxes ◽  
Lagrangian Particle ◽  
Global Fields ◽  
Mixing Ratios ◽  
Model Based

<p>The identification of the baseline is an important task in inverse modeling of greenhouse gases, as it represents the influence of atmospheric chemistry and transport and surface fluxes from outside the inversion domain, or flux contributions prior to the length of the backward calculation for Lagrangian models. When modeling halocarbons, observation-based approaches are often used to calculate the baseline, although model-based approaches are an alternative. Model-based methods need global unbiased fields of mixing ratios of the observed species, which are not always easy to get and which need to be interfaced with the model used for the inversion. To find the best way to identify the baseline and to investigate whether the usage of observation-based approaches is suitable for inverse modeling of halocarbons, we use and analyze a model-based and two frequently used observation-based methods to determine the baseline and investigate their influence on inversion results. The model-based method couples global fields of mixing ratios with backwards-trajectories at their point of termination. We simulate those global fields with a Lagrangian particle dispersion model, FLEXPART_CTM, that uses a nudging routine to relax model data to observed values. The second method under investigation is the robust estimation of baseline signal (REBS) method, that is purely based on statistical analysis of observations. The third analyzed method is also primarily observation-based, but uses model information to subtract prior simulated mixing ratios from selected observations. We apply those three methods to sulfur hexafluoride (SF<sub>6</sub>) and use the Bayesian inversion framework FLEXINVERT for the inverse modeling and the Lagrangian particle dispersion model FLEXPART to calculate the source-receptor-relationship used in the inversion.</p>

scholarly journals Assessing the regional surface influence through Backward Lagrangian Dispersion Models for aircraft CO<sub>2</sub> vertical profiles observations in NE Spain

2010 ◽  
Vol 10 (3) ◽  
pp. 8103-8134
Author(s):  
A. Font ◽  
J.-A. Morguí ◽  
X. Rodó
Keyword(s):  
Regional Scale ◽  
Grid Cell ◽  
Particle Dispersion ◽  
Vertical Profiles ◽  
Lagrangian Particle ◽  
Mixing Ratios ◽  
Continental Scale ◽  
Lagrangian Dispersion ◽  
Threshold Criteria ◽  
Ne Spain

Abstract. A weekly climatology for 2006 composed of 96-h-backward Lagrangian Particle Dispersion simulations is presented for nine aircraft sites measuring vertical profiles of atmospheric CO2 mixing ratios along the 42° N parallel in NE Spain to assess the surface influence at a regional scale (102–103 km) at different altitudes in the vertical profile (600, 1200, 2500 and 4000 meters above the sea level, m a.s.l.). The Potential Surface Influence (PSI) area for the 96-h-backward simulations, defined as the air layer above ground with a thickness of 300 m, are reduced from the continental scale (~107 km2) to the watershed one (~104 km2), when a Residence Time Threshold Criteria (Rttc) greater than 500 s is imposed for each grid cell. In addition, this regional restricted information is confined during 50 h before the arrival for simulations centered at 600 and 1200 m a.s.l. At higher altitudes (2500 and 4000 m a.s.l.), the regional surface influence is only recovered during spring and summer months. For simulations centered at 600 and 1200 m a.s.l. sites separated by ~60 km may overlap 20–50% of the regional surface influences whereas sites separated by ~350 km as such do not overlap. The overlap for sites separated by ~60 km decreases to 8–40% at higher altitudes (2500 and 4000 m a.s.l.). A dense network of sampling sites below 2200 m a.s.l. (whether aircraft sites or tall tower ones) guarantees an appropriate regional coverage to properly assess the dynamics of the regional carbon cycle at a watershed scale (102–103 km length scale).

Tracking source area of Shangdianzi station using Lagrangian particle dispersion model of FLEXPART

2013 ◽  
Vol 21 (3) ◽  
pp. 466-473 ◽  
Author(s):  
Xingqin An ◽  
Bo Yao ◽  
Yan Li ◽  
Nan Li ◽  
Lingxi Zhou
Keyword(s):  
Dispersion Model ◽  
Source Area ◽  
Particle Dispersion ◽  
Lagrangian Particle

Simulation of a dense gas chlorine release with a Lagrangian particle dispersion model (LPDM)

2021 ◽  
Vol 244 ◽  
pp. 117791 ◽  
Author(s):  
Félix Gomez ◽  
Bruno Ribstein ◽  
Laurent Makké ◽  
Patrick Armand ◽  
Jacques Moussafir ◽  
...  
Keyword(s):  
Dispersion Model ◽  
Particle Dispersion ◽  
Dense Gas ◽  
Lagrangian Particle
Sign in / Sign up

Export Citation Format

Share Document