Cite as:
R&ouml;sner, B.; Egli, S.; Thies, B.; Beyer, T.; Callies, D.; Pauscher, L. &amp; Bendix, J. (2020): <b>Fog and Low Stratus Obstruction of Wind Lidar Observations in Germany—A Remote Sensing-Based Data Set for Wind Energy Planning</b>. <i>Energies</i> <b>13</b>(15), 3859.

Resource Description

Title: Fog and Low Stratus Obstruction of Wind Lidar Observations in Germany—A Remote Sensing-Based Data Set for Wind Energy Planning
FOR816dw ID: 387
Publication Date: 2020-01-01
License and Usage Rights:
Resource Owner(s):
Individual: Benjamin Rösner
Individual: Sebastian Egli
Individual: Boris Thies
Individual: Tina Beyer
Individual: Doron Callies
Individual: Lukas Pauscher
Individual: Jörg Bendix
Coherent wind doppler lidar (CWDL) is a cost-effective way to estimate wind power<br/> potential at hub height without the need to build a meteorological tower. However, fog and low<br/> stratus (FLS) can have a negative impact on the availability of lidar measurements. Information<br/> about such reductions in wind data availability for a prospective lidar deployment site in advance is<br/> beneficial in the planning process for a measurement strategy. In this paper, we show that availability<br/> reductions by FLS can be estimated by comparing time series of lidar measurements, conducted<br/> with WindCubes v1 and v2, with time series of cloud base altitude (CBA) derived from satellite<br/> data. This enables us to compute average maps (2006–2017) of estimated availability, including<br/> FLS-induced data losses for Germany which can be used for planning purposes. These maps show<br/> that the lower mountain ranges and the Alpine regions in Germany often reach the critical data<br/> availability threshold of 80% or below. Especially during the winter time special care must be taken<br/> when using lidar in southern and central regions of Germany. If only shorter lidar campaigns are<br/> planned (3–6 months) the representativeness of weather types should be considered as well, because<br/> in individual years and under persistent weather types, lowland areas might also be temporally<br/> affected by higher rates of data losses. This is shown by different examples, e.g., during radiation fog<br/> under anticyclonic weather types.
| fog | wind energy | wind LiDAR |
Literature type specific fields:
Journal: Energies
Volume: 13
Issue: 15
Page Range: 3859
Metadata Provider:
Individual: Jörg Bendix
Online Distribution:
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