Gregor Giebel

Vertical momentum entrainment is driven by vertical shear and turbulent mixing and plays a key role in the recovery of wind turbine and wind-farm wakes, but remains poorly documented by field measurements. The LOLland offshore Lidar EXperiment (LOLLEX) campaign introduced a novel measurement approach to address this knowledge gap. The primary objective of this campaign was to develop a new atmospheric measurement strategy using Doppler wind lidar technology to better understand atmospheric conditions favourable to enhanced momentum entrainment inside and outside an offshore wind farm. LOLLEX was conducted from September 2022 to August 2023 in Denmark, in and around the Rødsand II wind farm, just south of the island of Lolland. During this campaign, two pulsed Doppler wind lidars, a scanning lidar (WindCube100S) and a lidar wind profiler (WindCubeV2), were deployed onboard a crew transfer vessel (CTV) commuting daily between the harbour and the Rødsand II offshore wind farm. Additionally, a scanning pulsed Doppler wind lidar (Halo Photonics) was mounted on a transformer platform north of the wind farm to perform range height indicator scans across the farm. Horizontal wind speed data were collected up to 300 m above the sea surface by the lidar wind profiler. The scanning lidar on the CTV collected data up to 2.5 km, alternating between the wind profiling mode and vertical stare mode. The latter scan operated at a sampling frequency of 1 Hz and along-beam spatial resolution of 10 m, allowing for the study of the turbulent vertical wind velocity component. The dataset includes several thousand hours of vertical scans. As a result of the moving vessel, many of the observations occurred inside or near the wind farm, providing insight into the near and far wakes of individual and multiple turbines. The potential and limitations of the new measurement strategy are illustrated using four case studies: (1) the observation of a Kelvin-Helmholtz instability above the wind farm, examined further in a companion paper; (2) turbulent mixing propagating downward from the top of the boundary layer; (3) internal atmospheric waves; and (4) wake characterisation inside the wind farm using the range-height indicator scans from the lidar deployed on the platform. This work demonstrates a novel methodology integrating remote sensing with a mobile offshore platform to measure turbulence at unprecedented altitudes. The dataset offers valuable data for wind energy research, boundary-layer meteorology, and further development of atmospheric measurement techniques.

Vertical momentum entrainment above offshore wind farms plays a key role in the recovery of wind turbine and wind farm wakes but remains poorly documented by field measurements. The LOLland offshore Lidar EXperiment (LOLLEX) campaign introduced a novel measurement approach to address this knowledge gap. The primary objective of this campaign was to develop a new atmospheric measurement strategy to characterise and quantify the vertical momentum entrainment inside and outside an offshore wind farm using Doppler wind lidar technology. LOLLEX was conducted from September 2022 to September 2023 in Denmark in and around the Rødsand II wind farm just south of the island of Lolland. During this campaign, two pulsed Doppler wind lidars, a scanning and a profiling instrument, were deployed onboard a crew transfer vessel (CTV) commuting daily between the harbour and the offshore wind farm Rødsand II. Additionally, a scanning pulsed Doppler wind lidar was mounted on a transformer platform north of the wind farm to perform range height indicator scans across the farm. Motion-corrected mean wind speed data were collected up to 300 m above the sea surface by the profiler lidar. The scanning lidar collected data up to 2.5 km alternating between the profiling mode and vertical stare mode. The latter scan operated with a sampling frequency of 1 Hz and along-beam spatial resolution of 10 m, allowing for the study of the turbulent vertical wind velocity component. The dataset includes several thousand hours of vertical scans. As a result of the moving vessel, many of the observations occurred inside or in the close vicinity of the wind farm, providing insight into the near and far wakes of individual and multiple turbines. The potential and limitations of the new measurement strategy is illustrated using four case studies: (1) the observation of a Kelvin–Helmholtz instability above the wind farm, examined further in a companion paper; (2) turbulent mixing propagating downward from the top of the boundary layer, enhancing momentum entrainment; (3) internal atmospheric waves and (4) wake characterisation inside the wind farm using the range-height indicator scans from the lidar deployed on the platform. This work demonstrates a novel methodology integrating remote sensing with a mobile offshore platform to measure turbulence at unprecedented altitudes. The dataset offers valuable data for wind energy research, boundary-layer meteorology, and further development of atmospheric measurement techniques.

Vertical momentum entrainment above offshore wind farms plays a key role in the recovery of wind turbine and wind farm wakes but remains poorly documented by field measurements. The LOLland offshore Lidar EXperiment (LOLLEX) campaign introduced a novel measurement approach to address this knowledge gap. The primary objective of this campaign was to develop a new atmospheric measurement strategy to characterise and quantify the vertical momentum entrainment inside and outside an offshore wind farm using Doppler wind lidar technology. LOLLEX was conducted from September 2022 to September 2023 in Denmark in and around the Rødsand II wind farm just south of the island of Lolland. During this campaign, two pulsed Doppler wind lidars, a scanning and a profiling instrument, were deployed onboard a crew transfer vessel (CTV) commuting daily between the harbour and the offshore wind farm Rødsand II. Additionally, a scanning pulsed Doppler wind lidar was mounted on a transformer platform north of the wind farm to perform range height indicator scans across the farm. Motion-corrected mean wind speed data were collected up to 300 m above the sea surface by the profiler lidar. The scanning lidar collected data up to 2.5 km alternating between the profiling mode and vertical stare mode. The latter scan operated with a sampling frequency of 1 Hz and along-beam spatial resolution of 10 m, allowing for the study of the turbulent vertical wind velocity component. The dataset includes several thousand hours of vertical scans. As a result of the moving vessel, many of the observations occurred inside or in the close vicinity of the wind farm, providing insight into the near and far wakes of individual and multiple turbines. The potential and limitations of the new measurement strategy is illustrated using four case studies: (1) the observation of a Kelvin–Helmholtz instability above the wind farm, examined further in a companion paper; (2) turbulent mixing propagating downward from the top of the boundary layer, enhancing momentum entrainment; (3) internal atmospheric waves and (4) wake characterisation inside the wind farm using the range-height indicator scans from the lidar deployed on the platform. This work demonstrates a novel methodology integrating remote sensing with a mobile offshore platform to measure turbulence at unprecedented altitudes. The dataset offers valuable data for wind energy research, boundary-layer meteorology, and further development of atmospheric measurement techniques.

Vertical momentum entrainment above offshore wind farms plays a key role in the recovery of wind turbine and wind farm wakes but remains poorly documented by field measurements. The LOLland offshore Lidar EXperiment (LOLLEX) campaign introduced a novel measurement approach to address this knowledge gap. The primary objective of this campaign was to develop a new atmospheric measurement strategy to characterise and quantify the vertical momentum entrainment inside and outside an offshore wind farm using Doppler wind lidar technology. LOLLEX was conducted from September 2022 to September 2023 in Denmark in and around the Rødsand II wind farm just south of the island of Lolland. During this campaign, two pulsed Doppler wind lidars, a scanning and a profiling instrument, were deployed onboard a crew transfer vessel (CTV) commuting daily between the harbour and the offshore wind farm Rødsand II. Additionally, a scanning pulsed Doppler wind lidar was mounted on a transformer platform north of the wind farm to perform range height indicator scans across the farm. Motion-corrected mean wind speed data were collected up to 300 m above the sea surface by the profiler lidar. The scanning lidar collected data up to 2.5 km alternating between the profiling mode and vertical stare mode. The latter scan operated with a sampling frequency of 1 Hz and along-beam spatial resolution of 10 m, allowing for the study of the turbulent vertical wind velocity component. The dataset includes several thousand hours of vertical scans. As a result of the moving vessel, many of the observations occurred inside or in the close vicinity of the wind farm, providing insight into the near and far wakes of individual and multiple turbines. The potential and limitations of the new measurement strategy is illustrated using four case studies: (1) the observation of a Kelvin–Helmholtz instability above the wind farm, examined further in a companion paper; (2) turbulent mixing propagating downward from the top of the boundary layer, enhancing momentum entrainment; (3) internal atmospheric waves and (4) wake characterisation inside the wind farm using the range-height indicator scans from the lidar deployed on the platform. This work demonstrates a novel methodology integrating remote sensing with a mobile offshore platform to measure turbulence at unprecedented altitudes. The dataset offers valuable data for wind energy research, boundary-layer meteorology, and further development of atmospheric measurement techniques.

The energy system needs a range of forecast types for its operation in addition to the narrow wind power forecast. Therefore, the notionally largest group world-wide discussing renewable forecasts, IEA Wind Task 51 “Forecasting for the Weather Driven Energy System” is reaching out to other IEA Technology Collaboration Programmes such as the ones for PV, hydropower, system integration, hydrogen etc. The three existing Work Packages (WPs) on NWP Improvements (WP1), Power and Uncertainty Forecasting (WP2) and optimal use of Forecasting Solutions (WP3), are complemented by thirteen work streams in a matrix structure. The three work packages span three distinct areas of challenge in forecasting for the weather driven energy system. The first area is the continuing effort to improve the representation of physical processes in weather forecast models through both new high performance initializations and tailored parameterizations. The second area is the heterogeneity of the forecasters and end users, the full understanding of the uncertainties throughout the modelling chain and the incorporation of novel data into power forecasting algorithms. A third area is representation, communication, and use of these uncertainties to industry in forms that readily support decision-making in plant operations and electricity markets. Task 51 focuses on facilitating communication and collaborations among international research groups engaged in the improvement of the accuracy and applicability of forecast models and their utility for the stakeholders in the wind industry, in the power sector and in the energy system. The collaboration is also structured in work streams, more targeted around a particular topic and potentially spanning several work packages [1]. Two of those work streams are aligned around forecasting horizons, the one on Sub-seasonal to Seasonal (S2S) forecasting and the one on minute-scale forecasting. Both work streams had public workshops. The Seasonal Forecasting workshop was in Reading (UK) in May 2023, while the Minute Scale Forecasting workshop was on 10/11 April 2024 in Risø (DK). While the S2S workshop was done in conjunction with WMO, the Minute Scale workshop had people from several other IEA Wind Tasks (Lidars, Wind Farm Flow Control and Hybrid Power Plants) as well as representatives of IEA PVPS Task 16 for the solar side in the committee. The poster will discuss the results of both workshops. Reference: [1] https://www.iea-wind.org/task51/ The Task website, last accessed 10 January 2024

Wind farms affect the atmosphere due to blockage, wakes and speed-ups. These modifications also affect other meteorological variables such as temperature, humidity and clouds locally under certain meteorological conditions. Therefore, there is a need to include wind farm effects in weather forecasts both for good-quality power predictions, but also for improved weather forecast in general.To evaluate the influence of currently existing on- and offshore wind turbines in Europe, we perform forecasts with the operational NWP model HARMONIE-AROME. The HARMONIE-AROME model is equipped with two wind farm parameterizations (WFPs), namely the WFP by Fitch et al. (2012) implemented by van Stratum et al. (2022) and the Explicit Wake Parameterization (EWP) by Volker et al. (2015) implemented by Fischereit et al. (2024).To represent the existing wind turbines in the simulations, we assembled an European wind turbine database for existing on- and offshore turbines that contains turbine locations and turbine characteristics such as hub height, rotor diameter and thrust curves. For the database we combined seven different data sets with a machine learning gap-filling approach to fill missing information.Using the database, we simulate a winter and a summer month with both WFP and compare them to a control simulation without wind farms for central and northern Europe. The simulations indicate that wind speed, temperature and humidity are affected locally by the presence of wind turbines. The wind farm effects differ in magnitude and sometimes in sign for the two WFPs. Fischereit, J., Vedel, H., Theeuwes, N. E., Larsén, X. G., Giebel, G., & Kaas, E. (2024). Modelling wind farm effects in HARMONIE-AROME - part 1: Implementation and evaluation. Geosci. Model Dev., 17, 2855–2875, https://doi.org/10.5194/gmd-17-2855-2024, 2024Fitch, A. C., Olson, J. B., Lundquist, J. K., Dudhia, J., Gupta, A. K., Michalakes, J., & Barstad, I. (2012). Local and Mesoscale Impacts of Wind Farms as Parameterized in a Mesoscale NWP Model. Monthly Weather Review, 140(9), 3017–3038. https://doi.org/10.1175/MWR-D-11-00352.1van Stratum, B., Theeuwes, N., Barkmeijer, J., van Ulft, B., & Wijnant, I. (2022). A one-year-long evaluation of a wind-farm parameterization in HARMONIE-AROME. Journal of Advances in Modeling Earth Systems, 14, e2021MS002947. https://doi.org/10.1029/2021MS002947Volker, P. J. H., Badger, J., Hahmann, A. N., & Ott, S. (2015). The Explicit Wake Parametrisation V1.0: a wind farm parametrisation in the mesoscale model WRF. Geoscientific Model Development, 8(11), 3715–3731. https://doi.org/10.5194/gmd-8-3715-2015

With increasing number and proximity of wind farms, it becomes crucial to consider wind farm effects (WFEs) in the numerical weather prediction (NWP) models used to forecast power production. Furthermore, these WFEs are also expected to affect other weather-related parameters at least locally. Thus, we implement the explicit wake parameterization (EWP) in the NWP model HARMONIE–AROME (hereafter HARMONIE) along-side the existing wind farm parameterization (WFP) by Fitch et al. (2012) (FITCH). We evaluate and compare the two WFPs against research flight measurements as well as against similar simulations performed with the Weather Research and Forecasting (WRF) model using case studies. The case studies include a case for WFEs above a wind farm as well as two cases for WFEs at hub height in the wake of farms. The results show that EWP and FITCH have been correctly implemented in HARMONIE. For the simulated cases, EWP underestimates the WFEs on wind speed and strongly underestimates the effect on turbulent kinetic energy (TKE). FITCH agrees better with the observations, and WFEs on TKE are particularly well captured by HARMONIE–FITCH. After this successful evaluation, simulations with all wind turbines in Europe will be performed with HARMONIE and presented in the second part of this paper series.

With increasing number and proximity of wind farms it becomes crucial to consider wind farm effects (WFE) in the numerical weather prediction (NWP) models used to forecast power production. Furthermore, these WFE are also expected to affect other weather-related parameters at least locally. Thus, we implement the explicit wake parameterization (EWP) in the NWP model HARMONIE-AROME (hereafter HARMONIE) along-side the existing wind farm parameterization (WFP) by Fitch et al. (2012) (FITCH). We evaluate and compare the two WFPs against research flight measurements as well as against similar simulations performed with the Weather Research and Forecasting model (WRF) using case studies. The case studies include a case for WFE above a wind farm as well as two cases for WFE at hub height in the wake of farms. The results show that EWP and FITCH have been correctly implemented in HARMONIE. For the simulated cases, EWP underestimates the WFE on wind speed and strongly underestimates the effect on turbulent kinetic energy (TKE). FITCH agrees better with the observations and WFE on TKE are particularly well captured by HARMONIE-FITCH. After this successful evaluation, simulations with all wind turbines in Europe will be performed with HARMONIE and presented in the second part of this paper series.

The energy system needs a range of forecast types for its operation in addition to the narrow wind power forecast. Therefore, the group behind the former IEA Wind Task 36 Forecasting for Wind Energy (running 6 years, from 2016-2021) has broadened its perspective on forecasting issues in part by reaching out to other IEA Technology Collaboration Programmes such as the ones for PV, hydropower, system integration, hydrogen etc. The three existing Work Packages (WPs) on NWP Improvements (WP1), Power and Uncertainty Forecasting (WP2) and optimal use of Forecasting Solutions (WP3), are complemented by thirteen work streams in a matrix structure. The three work packages span three distinct areas of challenge in forecasting for the weather driven energy system. The first area is the continuing effort to improve the representation of physical processes in weather forecast models through both new high performance initializations and tailored parameterizations. The second area is the heterogeneity of the forecasters and end users, the full understanding of the uncertainties throughout the modelling chain and the incorporation of novel data into power forecasting algorithms. A third area is representation, communication, and use of these uncertainties to industry in forms that readily support decision-making in plant operations and electricity markets. Task 51 will focus on facilitating communication and collaborations among international research groups engaged in the improvement of the accuracy and applicability of forecast models and their utility for the stakeholders in the wind industry, in the power sector and in the energy system. The first two activities of Task 51 were (1) a workshop in Dublin on the State of the Art and Research Gaps for Forecasting. The results of the workshop will be compiled into a journal article, and (2) the publication of the IEA Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions as an open access book by Elsevier. Other planned activities include further workshops on seasonal forecasting with emphasis on Dunkelflaute, storage and hydro in May 2023, a workshop on minute-scale forecasting (2024), and a workshop on extreme power system events (2025). The results of these interactive workshops will be compiled into a journal articles. Additionally, the Recommended Practice on Forecast Solution Selection will be updated to reflect the broader perspective. Reference: Corinna Möhrlen, John Zack, Gregor Giebel (eds): IEA Wind Recommended Practice for the Implementation of Renewable Energy Forecasting Solutions. Elsevier, 348 pages, Nov. 2022. ISBN: 9780443186813. Download the individual chapters from https://www.sciencedirect.com/book/9780443186813/iea-wind-recommended-practice-for-the-implementation-of-renewable-energy-forecasting-solutions.