The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
Andreas Stohl - One of the best experts on this subject based on the ideXlab platform.
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Source–receptor matrix calculation for Deposited Mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode
Geoscientific Model Development, 2017Co-Authors: Sabine Eckhardt, Massimo Cassiani, Nikolaos Evangeliou, Espen Sollum, Ignacio Pisso, Andreas StohlAbstract: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.
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source receptor matrix calculation for Deposited Mass with the lagrangian particle dispersion model flexpart v10 2 in backward mode
Geoscientific Model Development Discussions, 2017Co-Authors: Sabine Eckhardt, Massimo Cassiani, Nikolaos Evangeliou, Espen Sollum, Ignacio Pisso, Andreas StohlAbstract: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.
George Nikolich - One of the best experts on this subject based on the ideXlab platform.
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Trapping of Sand-Sized Particles Exterior and Interior to Large Porous Roughness Forms in the Atmospheric Surface Layer
Boundary-Layer Meteorology, 2019Co-Authors: John A. Gillies, Vic Etyemezian, George NikolichAbstract:Six same-sized, double-walled, porous cubes constructed of plastic mesh material of different porosity ε were deployed at a field site where they interacted with wind-driven saltation to evaluate their relative potential for sand sequestration (internal and external). The internal Mass collected and externally Deposited Mass and lengths demonstrate that, for large three-dimensional porous forms with both well-defined geometric shape and dimensional properties of the permeable walls, sequestration of saltating sand is largely controlled by the characteristic three-dimensional permeability K′ and the hydraulic diameter Hd of the wall material and not simply the value of ε of the walls of the forms. These two properties collapse the relationships for the particle-sequestration effectiveness [i.e., the (internal) trapping efficiency, normalized (external) deposit length, and normalized (external) deposit Mass] for the five forms with geometrically-similar square/rhomboid-shaped pores. The form with rounded-rectangular holes and very thin walls does not correspond to the same relationship, suggesting that pore geometry plays a key role in the magnitude of the amount of sand sequestered, as the data from the form with the differently-shaped pores are consistent outliers compared with the other five forms with similarly-shaped pores. This is due to the physical properties of the form, the pore shape, and the shape of the solid material around the pore, as the change in flow speed between the exterior and interior scales continually as a function of permeability K′, with no apparent effect related to the pore geometry.
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Trapping of Sand-Sized Particles Exterior and Interior to Large Porous Roughness Forms in the Atmospheric Surface Layer
Boundary-Layer Meteorology, 2018Co-Authors: John A. Gillies, Vic Etyemezian, George NikolichAbstract:Six same-sized, double-walled, porous cubes constructed of plastic mesh material of different porosity e were deployed at a field site where they interacted with wind-driven saltation to evaluate their relative potential for sand sequestration (internal and external). The internal Mass collected and externally Deposited Mass and lengths demonstrate that, for large three-dimensional porous forms with both well-defined geometric shape and dimensional properties of the permeable walls, sequestration of saltating sand is largely controlled by the characteristic three-dimensional permeability K′ and the hydraulic diameter Hd of the wall material and not simply the value of e of the walls of the forms. These two properties collapse the relationships for the particle-sequestration effectiveness [i.e., the (internal) trapping efficiency, normalized (external) deposit length, and normalized (external) deposit Mass] for the five forms with geometrically-similar square/rhomboid-shaped pores. The form with rounded-rectangular holes and very thin walls does not correspond to the same relationship, suggesting that pore geometry plays a key role in the magnitude of the amount of sand sequestered, as the data from the form with the differently-shaped pores are consistent outliers compared with the other five forms with similarly-shaped pores. This is due to the physical properties of the form, the pore shape, and the shape of the solid material around the pore, as the change in flow speed between the exterior and interior scales continually as a function of permeability K′, with no apparent effect related to the pore geometry.
Sabine Eckhardt - One of the best experts on this subject based on the ideXlab platform.
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Source–receptor matrix calculation for Deposited Mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode
Geoscientific Model Development, 2017Co-Authors: Sabine Eckhardt, Massimo Cassiani, Nikolaos Evangeliou, Espen Sollum, Ignacio Pisso, Andreas StohlAbstract: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.
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source receptor matrix calculation for Deposited Mass with the lagrangian particle dispersion model flexpart v10 2 in backward mode
Geoscientific Model Development Discussions, 2017Co-Authors: Sabine Eckhardt, Massimo Cassiani, Nikolaos Evangeliou, Espen Sollum, Ignacio Pisso, Andreas StohlAbstract: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.
Massimo Cassiani - One of the best experts on this subject based on the ideXlab platform.
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Source–receptor matrix calculation for Deposited Mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode
Geoscientific Model Development, 2017Co-Authors: Sabine Eckhardt, Massimo Cassiani, Nikolaos Evangeliou, Espen Sollum, Ignacio Pisso, Andreas StohlAbstract: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.
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source receptor matrix calculation for Deposited Mass with the lagrangian particle dispersion model flexpart v10 2 in backward mode
Geoscientific Model Development Discussions, 2017Co-Authors: Sabine Eckhardt, Massimo Cassiani, Nikolaos Evangeliou, Espen Sollum, Ignacio Pisso, Andreas StohlAbstract: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.
Nikolaos Evangeliou - One of the best experts on this subject based on the ideXlab platform.
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Source–receptor matrix calculation for Deposited Mass with the Lagrangian particle dispersion model FLEXPART v10.2 in backward mode
Geoscientific Model Development, 2017Co-Authors: Sabine Eckhardt, Massimo Cassiani, Nikolaos Evangeliou, Espen Sollum, Ignacio Pisso, Andreas StohlAbstract: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.
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source receptor matrix calculation for Deposited Mass with the lagrangian particle dispersion model flexpart v10 2 in backward mode
Geoscientific Model Development Discussions, 2017Co-Authors: Sabine Eckhardt, Massimo Cassiani, Nikolaos Evangeliou, Espen Sollum, Ignacio Pisso, Andreas StohlAbstract: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.