Articles | Volume 7, issue 6
https://doi.org/10.5194/gmd-7-2653-2014
https://doi.org/10.5194/gmd-7-2653-2014
Model description paper
 | 
13 Nov 2014
Model description paper |  | 13 Nov 2014

The photolysis module JVAL-14, compatible with the MESSy standard, and the JVal PreProcessor (JVPP)

R. Sander, P. Jöckel, O. Kirner, A. T. Kunert, J. Landgraf, and A. Pozzer

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Cited articles

Chabrillat, S. and Kockarts, G.: Simple parameterization of the absorption of the solar Lyman-alpha line, Geophys. Res. Lett., 24, 2659–2662, 1997.
Chabrillat, S. and Kockarts, G.: Correction to "Simple parameterization of the absorption of the solar Lyman-alpha line", Geophys. Res. Lett., 25, 79, 1998.
Danielache, S. O., Eskebjerg, C., Johnson, M. S., Ueno, Y., and Yoshida, N.: High-precision spectroscopy of 32S, 33S, and 34S sulfur dioxide: Ultraviolet absorption cross sections and isotope effects, J. Geophys. Res., 113, D17314, https://doi.org/10.1029/2007JD009695, 2008.
DeMore, W. B., Sander, S. P., Golden, D. M., Hampson, R. F., Kurylo, M. J., Howard, C. J., Ravishankara, A. R., Kolb, C. E., and Molina, M. J.: Chemical kinetics and photochemical data for use in stratospheric modeling. Evaluation number 12, JPL Publication 97-4, Jet Propulsion Laboratory, Pasadena, CA, 1997.
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