Articles | Volume 6, issue 6
https://doi.org/10.5194/gmd-6-2087-2013
https://doi.org/10.5194/gmd-6-2087-2013
Development and technical paper
 | 
16 Dec 2013
Development and technical paper |  | 16 Dec 2013

Correction of approximation errors with Random Forests applied to modelling of cloud droplet formation

A. Lipponen, V. Kolehmainen, S. Romakkaniemi, and H. Kokkola

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Cited articles

Abdul-Razzak, H., Ghan, S. J., and Rivera-Carpio, C.: A parameterization of aerosol activation 1. single aerosol type, J. Geophys. Res., 103, 6123–6131, https://doi.org/10.1029/97JD03735, 1998.
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation 2. Multiple aerosol types, J. Geophys. Res., 105, 6837–6844, https://doi.org/10.1029/1999JD901161, 2000.
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation 3. Sectional representation, J. Geophys. Res., 107, AAC 1-1–AAC 1-6, https://doi.org/10.1029/2001JD000483, 2002.
Arridge, S., Kaipio, J., Kolehmainen, V., Schweiger, M., Somersalo, E., Tarvainen, T., and Vauhkonen, M.: Approximation errors and model reduction with an application in optical diffusion tomography, Inverse Probl., 22, 175–195, https://doi.org/10.1088/0266-5611/22/1/010, 2006.
Bechtel, B. and Daneke, C.: Classification of local climate zones based on multiple earth observation data, IEEE J. Sel. Top. Appl., 5, 1191–1202, https://doi.org/10.1109/JSTARS.2012.2189873, 2012.
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