Airborne measurements of liquid water content (LWC) and ice water content (IWC) were performed in mixed-phase clouds during the field campaign VERDI in Canada in April and May 2012. In single-layer and multi-layer clouds different vertical profiles of LWC and IWC could be observed. For single layer clouds the maximum LWC occurred in the upper half of the clouds while the IWC had a maximum near the cloud base. This pattern was attributed to the sedimentation of ice particles. In the lowest cloud layer of a multi-layer system both LWC and IWC reached a maximum near cloud top. Together with measured particles size distributions this suggests the presence of the seeder-feeder-process described by Fleishauer et al. (2012) for mid-level clouds. / Im Rahmen der VERDI-Kampagne im April und Mai 2012 in Kanada wurden flugzeuggetragene Messungen von Flüssigwassergehalt (LWC) und Eiswassergehalt (IWC) durchgeführt. Für Einschicht- und Mehrschichtwolkensysteme konnten unterschiedliche Vertikalprofile von LWC und IWC nachgewiesen werden. In Einschichtsystemen wurden die größten Flüssigwassergehalte in der oberen Wolkenhälfte und die größten Eiswassergehalte nahe der Wolkenunterkante gemessen. Diese Verteilung wurde auf die Sedimentation von Eispartikeln zurückgeführt. In der untersten Wolkenschicht eines Mehrschichtsystems befanden sich die Maxima von LWC und IWC nahe der Wolkenoberkante. Diese Beobachtung deutet zusammen mit gemessenen Partikelgrößenverteilungen auf das Vorhandensein des Seeder-Feeder- Prozesses hin, welcher von Fleishauer et al. (2012) für mittelhohe Wolken beschrieben wurde.

This thesis is written as a cumulative dissertation. It presents methods and results which contribute to an improved understanding of the spatio-temporal variability of fog and fog deposition. The questions to be answered are: When is there how much fog, and where and how much fog is deposited on the vegetation as fog precipitation? Freely available data sets serve as a database. The meteorological input data are obtained from the Climate Data Center (CDC) of the German Meteorological Service (DWD). Station data for temperature, relative humidity and wind speed in hourly resolution are used. In addition, visibility data are used for validation purposes. Furthermore, Global Forest Heights (GFH) data from the National Aeronautics and Space Administration (NASA) are used as vegetation height data. The data from NASA’s Shuttle Radar Topography Mission (SRTM) is used as a digital elevation model. The first publication deals with gap filling and data compression for further calculations. This is necessary since the station density for hourly data is relatively low, especially before the 2000s. In addition, there are more frequent gaps in hourly data than in, for instance, daily data, which can thus be filled. It is shown that gradient boosting (gb) enables high quality gap filling in a short computing time. The second publication deals with the determination of the fog, especially with the liquid water content (lwc). Here the focus is on the correction of measurement errors of the relative humidity as well as methods of spatial interpolation are dealt with. The resulting lwc data for Germany with a temporal resolution of one hour and a spatial resolution of one kilometre, are validated against measured lwc data as well as visibility data of the DWD. The last publication uses the data and methods of the two previous publications. The vegetation and wind speed data are also used to determine fog precipitation from the lwc data. This is validated using data from other publications and water balance calculations. In addition to the measured precipitation, the fog precipitation data are used as an input variable for the modelling. This is also one of the possible applications: To determine precipitation from fog, which is not recorded by standard measuring methods, and thus to make water balance modelling more realistic.:1 MOTIVATION 6 2 PROBLEM DEFINITION AND TARGET SETTING 6 3 STRUCTURE 7 4 MODEL LIMITS 9 5 PUBLICATIONS 9 6 OUTLOOK 29