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

Benjamin Rösner, Sebastian Egli, Boris Thies, Tina Beyer, Doron Callies, Lukas Pauscher and Jörg Bendix
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Benjamin Rösner: Laboratory for Climatology and Remote Sensing (LCRS), University of Marburg, 35032 Marburg, Germany
Sebastian Egli: Laboratory for Climatology and Remote Sensing (LCRS), University of Marburg, 35032 Marburg, Germany
Boris Thies: Laboratory for Climatology and Remote Sensing (LCRS), University of Marburg, 35032 Marburg, Germany
Tina Beyer: Ramboll, 81541 Munich, Germany
Doron Callies: Fraunhofer IEE Kassel, 34119 Kassel, Germany
Lukas Pauscher: Fraunhofer IEE Kassel, 34119 Kassel, Germany
Jörg Bendix: Laboratory for Climatology and Remote Sensing (LCRS), University of Marburg, 35032 Marburg, Germany

Energies, 2020, vol. 13, issue 15, 1-13

Abstract: 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.

Keywords: wind; lidar; availability; fog; clouds (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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