Identification of cloud type and cloud phase through a combination of multiple groundbased sensors
Contribution Uni Köln (2)
At the Umweltstation Schneefernerhaus (UFS) on the Zugspitze (2650m) in southern
Germany the designated unique combination of remote sensing and in-situ instruments will allow to analyse in detail the composition of clouds and precipitation occurring in the vertical column above the site on a long-term basis. The goal of these observations is to evaluate the connections between the occurrence of ice clouds and solid/liquid precipitation in a statistical sense by identifying atmospheric conditions and physical processes that lead to significant precipitation. Especially the occurrence of sizes and habits of ice crystals in clouds and their influences on the initialization of precipitation will be examined. These observations are of high value to the remote sensing community because many satellite precipitation algorithms
(infrared, optical spectral regions) rely on indirect assumptions between the observed ice phase and the precipitation occurring below at surface levels. Ground-based observations like this will also be able to show the potential of EarthCARE (ESA Earth Explorer Mission, foreseen launch ~2012) observations for process studies since the high elevation observations at the UFS mostly guarantee very low water vapour and aerosol levels which obscure similar observations at lower height levels.
In the proposed work, we will take advantage of the existing Cloudnet target classification scheme (Illingworth et. al, 2007) developed at the University of Reading, UK. This scheme classifies the targets, which contribute to the backscattered radiation received either by the cloud radar or the lidar ceilometer (Fig. 1). With this classification scheme it is possible to discriminate if the backscattered radiation originates e.g. from liquid clouds, ice clouds, precipitating or non-precipitating clouds or even aerosols or insects. The radar and lidar observations are first averaged to a common grid and then supplemented by temperature, pressure, humidity and wind speed from an operational NWP model to assist with attenuation correction and cloud phase identification. The full details of how the backscatter targets in
Fig. 1 : An example 24-hour time series of the target classification scheme (Lindenberg, 22
October 2005) according to Hogan and O’Connor (2006). A target classification index is given below. From Löhnert et al., 2008

each radar/lidar pixel are then categorized into a number of different classes are given by
Hogan and O’Connor (2006), but essentially it makes use of the fact that radar is sensitive to large particles such as rain and drizzle drops, ice particles and insects, while the lidar is sensitive to higher concentrations of smaller particles such as cloud droplets and aerosol.
In this contribution we will develop a specifically adapted version of the Cloudnet scheme to account for the extensive information contained in the UFS measurements. Especially the available passive microwave measurements at frequencies at 90 GHz and higher are expected to be beneficial for the detection and composition of solid precipitation - a scientific issue which is currently be exploited within the DFG funded TOSCA project which involves the development of prototype algorithms for characterizing snowfall properties. Additionally to the permanent installation a cloud radar (which is included in the latest UFS Development
Plan), an X-Band micro-rain radar with Doppler capabilities will help to characterize the falling precipitation. Additionally ground-based in-situ measurements of crystal size and habit via a 2D video-disdrometer during dedicated measurement campaigns (see contribution (1),
Uni-Köln) will help to characterize the causalities of surface precipitation and ice cloud properties measured aloft. If MIM will participate I will include some more sentences about depolarization lidar here.
References
Hogan, R. J., and E. J. O'Connor, 2006: Facilitating cloud radar and lidar algorithms: the
Cloudnet Instrument Synergy/Target Categorization product. Cloudnet documentation: http://www.cloud-net.org/data/products/categorize.html
.
Illingworth, A. J., R. J. Hogan, E. J. O Connor, D. Bouniol, M. E. Brooks, J. Delanoë, P.
Donovan, J. D. Eastment, N. Gaussiat, J. W. F. Goddard, M. Haeffelin, H. Klein Baltink, O.
A. Krasnov, J. Pelon, J.-M. Piriou, A. Protat, H. W. J. Russchenberg, A. Seifert, A. M.
Tompkins, G.-J. van Zadelhoff, F. Vinit, U. Willén, D. R. Wilson, and C. L. Wrench, 2007:
CLOUDNET Continuous evaluation of cloud profiles in seven operational models using ground-based observations. Bull. Amer. Meteor. Sci.
, DOI:10.1175/BAMS-88-6-xxx
Löhnert, U., S. Crewell, O. Krasnov, E. O’Connor, H. Russchenberg, 2008: Advances in
Continuously Profiling the Thermodynamic State of the Boundary Layer: Integration of
Measurements and Methods, J. Atmos. Oceanic Technol. – in press