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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2018-259
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2018-259
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 02 Nov 2018

Methods for assessment of models | 02 Nov 2018

Model evaluation by a cloud classification based on multi-sensor observations

Akio Hansen1,2, Felix Ament1,2, Verena Grützun1, and Andrea Lammert1 Akio Hansen et al.
  • 1Meteorological Institute, University Hamburg, Bundesstraße 55, 20146 Hamburg, Germany
  • 2Max-Planck-Institute for Meteorology, Bundesstraße 53, 20146 Hamburg, Germany

Abstract. The detailed understanding of clouds and their macrophysical properties is crucial to reduce uncertainties of cloud feedbacks and related processes in current climate and weather prediction models. Comprehensive evaluation of cloud characteristics using observations is the first step towards any improvement.

An advanced observational product was developed by the Cloudnet project. A multi-sensor synergy of active and passive remote-sensing instruments is used to generate a Target Classification providing detailed information about cloud phase and structure. Nevertheless, this valuable product is only available for observations and there is yet no comparable surrogate for models. Therefore, a new cloud classification algorithm is presented to calculate a comparable classification for models by using the temperature, dew point and all hydrometeor profiles.

The study explains the algorithm and shows possible evaluation methods making use of the new synthetic cloud classification. For example, the statistics of the vertical cloud distribution as well as e.g. the accuracy of cloud forecasts can be investigated regarding different cloud types. The algorithm and methods are exemplarily tested on two months of operational weather forecast data of the COSMO-DE model and compared to a Cloudnet supersite in Germany. Additionally, the cloud classification is applied to Large Eddy Simulations with a similar resolution as of the observations showing detailed cloud structures.

Akio Hansen et al.
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Akio Hansen et al.
Data sets

Temperature, dew point and hydrometeor column output of ICON Large Eddy Simulation in Krauthausen (Aachen, Germany) on 2013-04-26. Akio Hansen, Felix Ament, Verena Grützun, and Andrea Lammert https://doi.pangaea.de/10.1594/PANGAEA.895303

HD(CP)2 short term observations, Cloudnet - Target Classification data of Cloudnet products (no. 00), HOPE campaign by LACROS, data version 01. Patric Seifert https://doi.org/10.17616/R3D944

Model code and software

Cloud Classification algorithm (Matlab version) with example dataset of COSMO-DE model. Akio Hansen, Felix Ament, Verena Grützun, and Andrea Lammert https://doi.org/10.5281/zenodo.1458145

Akio Hansen et al.
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Latest update: 25 Jun 2019
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Short summary
Clouds are responsible for large uncertainties in atmospheric models, whereby the evaluation is very challenging due to their complexity. The Cloudnet project uses multi-sensor observations to create a comprehensive Target Classification showing the cloud structure and phase, but there is no comparable model output available. The presented cloud classification algorithm generates a consistent product, which provides a comprehensive view on clouds and is used for further in-depth evaluation.
Clouds are responsible for large uncertainties in atmospheric models, whereby the evaluation is...
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