Articles | Volume 4, issue 1
https://doi.org/10.5194/gmd-4-1-2011
https://doi.org/10.5194/gmd-4-1-2011
Development and technical paper
 | 
11 Jan 2011
Development and technical paper |  | 11 Jan 2011

Meteorological and trace gas factors affecting the number concentration of atmospheric Aitken (Dp = 50 nm) particles in the continental boundary layer: parameterization using a multivariate mixed effects model

S. Mikkonen, H. Korhonen, S. Romakkaniemi, J. N. Smith, J. Joutsensaari, K. E. J. Lehtinen, A. Hamed, T. J. Breider, W. Birmili, G. Spindler, C. Plass-Duelmer, M. C. Facchini, and A. Laaksonen

Related subject area

Atmospheric sciences
Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025,https://doi.org/10.5194/gmd-18-1017-2025, 2025
Short summary
Accurate space-based NOx emission estimates with the flux divergence approach require fine-scale model information on local oxidation chemistry and profile shapes
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025,https://doi.org/10.5194/gmd-18-621-2025, 2025
Short summary
Exploring a high-level programming model for the NWP domain using ECMWF microphysics schemes
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025,https://doi.org/10.5194/gmd-18-529-2025, 2025
Short summary
Quantifying uncertainties in satellite NO2 superobservations for data assimilation and model evaluation
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025,https://doi.org/10.5194/gmd-18-483-2025, 2025
Short summary
ML-AMPSIT: Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025,https://doi.org/10.5194/gmd-18-433-2025, 2025
Short summary

Cited articles

Birmili, W. and Wiedensohler, A.: New particle formation in the continental boundary layer: Meteorological and gas phase parameter influence, Geophys. Res. Lett., 27, 3325–3328, 2000.
Birmili, W., Wiedensohler, A., Heintzenberg, J., and Lehmann, K.: Atmospheric particle number size distribution in Central Europe: statistical relations to air masses and meteorology, J. Geophys. Res., 106, 32005-32018, https://doi.org/10.1029/2000JD000220, 2001.
Birmili, W., Berresheim, H., Plass-Dülmer, C., Elste, T., Gilge, S., Wiedensohler, A., and Uhrner, U.: The Hohenpeissenberg aerosol formation experiment (HAFEX): a long-term study including size-resolved aerosol, H2SO4, OH, and monoterpenes measurements, Atmos. Chem. Phys., 3, 361–376, https://doi.org/10.5194/acp-3-361-2003, 2003.
Boy, M. and Kulmala, M.: Nucleation events in the continental boundary layer: Influence of physical and meteorological parameters, Atmos. Chem. Phys., 2, 1–16, https://doi.org/10.5194/acp-2-1-2002, 2002.
Chipperfield, M. P.: New version of the TOMCAT/SLIMCAT offline chemical transport model: Intercomparison of stratospheric tracer experiments, Q. J. R Meteorol. Soc., 132, 1179–1203, https://doi.org/10.1256/qj.05.51, 2006.
Download
Share