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Fog and Low Stratus Obstruction of Wind Lidar Observations in Germany - A Remote Sensing-Based Data Set for Wind Energy Planning

: Rösner, Benjamin; Egli, Sebastian; Thies, Boris; Beyer, Tina; Callies, Doron; Pauscher, Lukas; Bendix, Jörg

Fulltext ()

Energies 13 (2020), No.15, Art. 3859, 13 pp.
ISSN: 1996-1073
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
6. EFP; 0324159A; LiMeS
Optimierung von LiDAR basierten Messstrategien zur Standortbewertung von Windparks
Journal Article, Electronic Publication
Fraunhofer IEE ()
wind; LiDAR; availability; fog; clouds

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.