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GMD | Articles | Volume 11, issue 10
Geosci. Model Dev., 11, 4195–4214, 2018
https://doi.org/10.5194/gmd-11-4195-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 4195–4214, 2018
https://doi.org/10.5194/gmd-11-4195-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 16 Oct 2018

Methods for assessment of models | 16 Oct 2018

(GO)2-SIM: a GCM-oriented ground-observation forward-simulator framework for objective evaluation of cloud and precipitation phase

Katia Lamer et al.
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Cited articles  
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Atlas, D., Matrosov, S. Y., Heymsfield, A. J., Chou, M.-D., and Wolff, D. B.: Radar and radiation properties of ice clouds, J. Appl. Meteorol., 34, 2329–2345, 1995. 
Battaglia, A. and Delanoë, J.: Synergies and complementarities of CloudSat-CALIPSO snow observations, J. Geophys. Res.-Atmos., 118, 721–731, 2013. 
Battan, L. J.: Radar observation of the atmosphere, University of Chicago, Chicago, Illinois, 1973. 
Bodas-Salcedo, A., Webb, M., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S., Zhang, Y., Marchand, R., Haynes, J., and Pincus, R.: COSP: Satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, 2011. 
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Weather and climate predictions of cloud, rain, and snow occurrence remain uncertain, in part because guidance from observation is incomplete. We present a tool that transforms predictions into observations from ground-based remote sensors. Liquid water and ice occurrence errors associated with the transformation are below 8 %, with ~ 3 % uncertainty. This (GO)2-SIM forward-simulator tool enables better evaluation of cloud, rain, and snow occurrence predictions using available observations.
Weather and climate predictions of cloud, rain, and snow occurrence remain uncertain, in part...
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