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

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.