Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 5.154 IF 5.154
  • IF 5-year value: 5.697 IF 5-year
    5.697
  • CiteScore value: 5.56 CiteScore
    5.56
  • SNIP value: 1.761 SNIP 1.761
  • IPP value: 5.30 IPP 5.30
  • SJR value: 3.164 SJR 3.164
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 59 Scimago H
    index 59
  • h5-index value: 49 h5-index 49
Volume 10, issue 6
Geosci. Model Dev., 10, 2365–2377, 2017
https://doi.org/10.5194/gmd-10-2365-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 10, 2365–2377, 2017
https://doi.org/10.5194/gmd-10-2365-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Methods for assessment of models 27 Jun 2017

Methods for assessment of models | 27 Jun 2017

STRAPS v1.0: evaluating a methodology for predicting electron impact ionisation mass spectra for the aerosol mass spectrometer

David O. Topping et al.

Related authors

Quantifying Bioaerosol Concentrations in Dust Clouds through Online UV-LIF and Mass Spectrometry Measurements at the Cape Verde Atmospheric Observatory
Douglas Morrison, Ian Crawford, Nicholas Marsden, Michael Flynn, Katie Read, Luis Neves, Virginia Foot, Paul Kaye, Warren Stanley, Hugh Coe, David Topping, and Martin Gallagher
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-157,https://doi.org/10.5194/acp-2020-157, 2020
Preprint under review for ACP
Short summary
A predictive group-contribution model for the viscosity of aqueous organic aerosol
Natalie R. Gervasi, David O. Topping, and Andreas Zuend
Atmos. Chem. Phys., 20, 2987–3008, https://doi.org/10.5194/acp-20-2987-2020,https://doi.org/10.5194/acp-20-2987-2020, 2020
Short summary
Dynamic Complex Network Analysis of PM2.5 Concentrations in the UK using Hierarchical Directed Graphs (V1.0.0)
Parya Broomandi, Xueyu Geng, Weisi Guo, Jong Ryeol Kim, Alessio Pagani, and David Topping
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2019-342,https://doi.org/10.5194/gmd-2019-342, 2020
Revised manuscript under review for GMD
Short summary
Measured solid state and sub-cooled liquid vapour pressures of nitroaromatics using Knudsen effusion mass spectrometry
Petroc D. Shelley, Thomas J. Bannan, Stephen D. Worrall, M. Rami Alfarra, Ulrich K. Krieger, Carl J. Percival, Arthur Garforth, and David Topping
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-75,https://doi.org/10.5194/acp-2020-75, 2020
Revised manuscript under review for ACP
Short summary
Modelling the effect of condensed-phase diffusion on the homogeneous nucleation of ice in ultra-viscous particles
Kathryn Fowler, Paul Connolly, and David Topping
Atmos. Chem. Phys., 20, 683–698, https://doi.org/10.5194/acp-20-683-2020,https://doi.org/10.5194/acp-20-683-2020, 2020
Short summary

Related subject area

Atmospheric Sciences
Development of a reduced-complexity plant canopy physics surrogate model for use in chemical transport models: a case study with GEOS-Chem v12.3.0
Sam J. Silva, Colette L. Heald, and Alex B. Guenther
Geosci. Model Dev., 13, 2569–2585, https://doi.org/10.5194/gmd-13-2569-2020,https://doi.org/10.5194/gmd-13-2569-2020, 2020
Short summary
An adaptive method for speeding up the numerical integration of chemical mechanisms in atmospheric chemistry models: application to GEOS-Chem version 12.0.0
Lu Shen, Daniel J. Jacob, Mauricio Santillana, Xuan Wang, and Wei Chen
Geosci. Model Dev., 13, 2475–2486, https://doi.org/10.5194/gmd-13-2475-2020,https://doi.org/10.5194/gmd-13-2475-2020, 2020
Short summary
Satellite-derived leaf area index and roughness length information for surface–atmosphere exchange modelling: a case study for reactive nitrogen deposition in north-western Europe using LOTOS-EUROS v2.0
Shelley C. van der Graaf, Richard Kranenburg, Arjo J. Segers, Martijn Schaap, and Jan Willem Erisman
Geosci. Model Dev., 13, 2451–2474, https://doi.org/10.5194/gmd-13-2451-2020,https://doi.org/10.5194/gmd-13-2451-2020, 2020
Short summary
An online emission module for atmospheric chemistry transport models: implementation in COSMO-GHG v5.6a and COSMO-ART v5.1-3.1
Michael Jähn, Gerrit Kuhlmann, Qing Mu, Jean-Matthieu Haussaire, David Ochsner, Katherine Osterried, Valentin Clément, and Dominik Brunner
Geosci. Model Dev., 13, 2379–2392, https://doi.org/10.5194/gmd-13-2379-2020,https://doi.org/10.5194/gmd-13-2379-2020, 2020
Short summary
Representing model uncertainty for global atmospheric CO2 flux inversions using ECMWF-IFS-46R1
Joe R. McNorton, Nicolas Bousserez, Anna Agustí-Panareda, Gianpaolo Balsamo, Margarita Choulga, Andrew Dawson, Richard Engelen, Zak Kipling, and Simon Lang
Geosci. Model Dev., 13, 2297–2313, https://doi.org/10.5194/gmd-13-2297-2020,https://doi.org/10.5194/gmd-13-2297-2020, 2020
Short summary

Cited articles

Aiken, A. C., DeCarlo, P. F., and Jimenez, J. L.: Elemental analysis of organic species with electron ionization high-resolution mass spectrometry, Anal. Chem., 79, 8350–8358, https://doi.org/10.1021/ac071150w, 2007.
Alfarra, M. R., Good, N., Wyche, K. P., Hamilton, J. F., Monks, P. S., Lewis, A. C., and McFiggans, G.: Water uptake is independent of the inferred composition of secondary aerosols derived from multiple biogenic VOCs, Atmos. Chem. Phys., 13, 11769–11789, https://doi.org/10.5194/acp-13-11769-2013, 2013.
Aumont, B., Szopa, S., and Madronich, S.: Modelling the evolution of organic carbon during its gas-phase tropospheric oxidation: development of an explicit model based on a self generating approach, Atmos. Chem. Phys., 5, 2497–2517, https://doi.org/10.5194/acp-5-2497-2005, 2005.
Aumont, B., Valorso, R., Mouchel-Vallon, C., Camredon, M., Lee-Taylor, J., and Madronich, S.: Modeling SOA formation from the oxidation of intermediate volatility n-alkanes, Atmos. Chem. Phys., 12, 7577–7589, https://doi.org/10.5194/acp-12-7577-2012, 2012.
Publications Copernicus
Download
Short summary
Our ability to model the chemical and thermodynamic processes that lead to secondary organic aerosol (SOA) formation is thought to be hampered by the complexity of the system. In this proof of concept study, the ability to train supervised methods to predict electron impact ionisation (EI) mass spectra for the AMS is evaluated to facilitate improved model evaluation. The study demonstrates the use of a methodology that would be improved with more training data and data from simple mixed systems.
Our ability to model the chemical and thermodynamic processes that lead to secondary organic...
Citation