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Coherent wind doppler lidar (CWDL) is a cost-effective way to estimate wind power potential at hub height without the need to build a meteorological tower. However, fog and low stratus (FLS) can have a negative impact on the availability of lidar measurements. Information about such reductions in wind data availability for a prospective lidar deployment site in advance is beneficial in the planning process for a measurement strategy. In this paper, we show that availability reductions by FLS can be estimated by comparing time series of lidar measurements, conducted with WindCubes v1 and v2, with time series of cloud base altitude (CBA) derived from satellite data. This enables us to compute average maps (2006-2017) of estimated availability, including FLS-induced data losses for Germany which can be used for planning purposes. These maps show that the lower mountain ranges and the Alpine regions in Germany often reach the critical data availability threshold of 80% or below. Especially during the winter time special care must be taken when using lidar in southern and central regions of Germany. If only shorter lidar campaigns are planned (3-6 months) the representativeness of weather types should be considered as well, because in individual years and under persistent weather types, lowland areas might also be temporally affected by higher rates of data losses. This is shown by different examples, e.g., during radiation fog under anticyclonic weather types.

Nebel hat eine große Anzahl wirtschaftlicher und ökologischer Auswirkungen und beeinflusst das menschliche Leben in vielerlei Hinsicht. Die Menge der Einflussfaktoren zeigt, dass ein umfassendes Verständnis seiner Bildungsursachen und eine genaue Erfassung seiner raumzeitlichen Verteilungsmuster von großem Interesse sind. Da es berechtigte Bedenken hinsichtlich der allgemeinen Anwendbarkeit bestehender Nebelerkennungsverfahren gibt, werden in dieser Arbeit neue Techniken der satellitengestützten Nebelerkennung untersucht und darauf aufbauend raumzeitliche Informationen über die Nebelverteilung in Europa abgeleitet. Die zentralen Neuentwicklungen dieser Arbeit sind: - Bei der Nebelerkennung wurden keine statischen Annahmen bezüglich der mikrophysikalischen Eigenschaften herangezogen. - Es wurde ein neuartiger hybrider Ansatz auf Basis von maschinellen Lernmethoden entwickelt, der 24 Stunden am Tag kontinuierlich angewendet werden kann. - Der Algorithmus berücksichtigt alle Nebeltypen. Regionen mit unterschiedlichen Nebeltypen konnten auf Basis des erzeugten Nebelprodukts aufgrund ihrer typischen Tages- und Jahresgänge indirekt voneinander unterschieden werden. - Erstmals konnten für Europa Nebelfrequenzkarten für verschiedene Großwetterlagen separat für jeden Nebeltyp erstellt werden., Fog has many economic as well as ecological impacts and it directly affects human life in many ways. The large number of fog influence factors shows that a comprehensive understanding of its causes and a precise mapping of the spatio-temporal distribution patterns are of great interest. Since there are justifiable concerns about the general applicability of existing fog retrieval methods, this thesis investigates new techniques of satellite based fog detection and the derivation of spatio-temporal information on fog distribution in Europe. The central novelties of this study are: - No static assumptions about microphysical properties were used during fog retrieval. - A novel hybrid approach based on machine learning methods was developed that can be continuously applied 24 hours a day. - The algorithm covers all fog types. Areas of different fog types could also be differentiated indirectly from the generated product due to their typical diurnal and annual frequency cycles. - For the first time, fog frequency maps for Europe could be produced for different general weather situations separately for each fog type.

Die Erde 147(1), 15-39 (2016)., Published by Gesellschaft für Erdkunde, Berlin