Articles | Volume 10, issue 8
Geosci. Model Dev., 10, 2971–2999, 2017
https://doi.org/10.5194/gmd-10-2971-2017

Special issue: Coupled chemistry–meteorology modelling: status and...

Geosci. Model Dev., 10, 2971–2999, 2017
https://doi.org/10.5194/gmd-10-2971-2017
Model description paper
08 Aug 2017
Model description paper | 08 Aug 2017

Enviro-HIRLAM online integrated meteorology–chemistry modelling system: strategy, methodology, developments and applications (v7.2)

Alexander Baklanov et al.

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Cited articles

Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation: 2. Multiple aerosol types, J. Geophys. Res.-Atmos., 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000.
Abdul-Razzak, H., Ghan, S. J., and Rivera-Carpio, C.: A parameterization of aerosol activation: 1. Single aerosol type, J. Geophys. Res.-Atmos., 103, 6123–6131, https://doi.org/10.1029/97JD03735, 1998.
Allen, L., Beevers, S., Lindberg, F., Iamarino, M., Kitiwiroon, N., and Grimmond, C.: Global to City Scale Urban Anthropogenic Heat Flux: Model and Variability, MEGAPOLI Scientific Report, Tech. Rep. 10–01, King's College London, Environmental Monitoring and Modelling Group, London, 2010.
Anderson, G. P., Clough, S. A., Kneizys, F. X., Chetwynd, J. H., and Shettle, E. P.: AFGL Atmospheric Constituent Profiles (0–120 km), Tech. Rep. AFGL-TR-86-0110, Air Force Geophysics Lab Hanscom AFB, MA, USA, 1986.
Andreae, M. O. and Merlet, P.: Emission of trace gases and aerosols from biomass burning, Global Biogeochem. Cy., 15, 955–966, https://doi.org/10.1029/2000GB001382, 2001.
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Short summary
The Environment – HIgh Resolution Limited Area Model (Enviro-HIRLAM) is developed as a fully online integrated numerical weather prediction and atmospheric chemical transport model for research and forecasting of joint meteorological, chemical and biological weather. Different aspects of online coupling methodology, research strategy and possible applications of the modelling system, and ''fit-for-purpose'' model configurations for the meteorological and air quality communities are discussed.