More than 12 GW of offshore wind turbines are currently in operation in European waters. To optimise the use of the marine areas, wind farms are typically clustered in units of several hundred turbines. Understanding wakes of wind farms, which is the region of momentum and energy deficit downwind, is important for optimising the wind farm layouts and operation to minimize costs. While in most weather situations (unstable atmospheric stratification), the wakes of wind turbines are only a local effect within the wind farm, satellite imagery reveals wind-farm wakes to be several tens of kilometres in length under certain conditions (stable atmospheric stratification), which is also predicted by numerical models. The first direct in situ measurements of the existence and shape of large wind farm wakes by a specially equipped research aircraft in 2016 and 2017 confirm wake lengths of more than tens of kilometres under stable atmospheric conditions, with maximum wind speed deficits of 40%, and enhanced turbulence. These measurements were the first step in a large research project to describe and understand the physics of large offshore wakes using direct measurements, together with the assessment of satellite imagery and models.
With the Arctic rapidly changing, the needs to observe, understand, and model the changes are essential. To support these needs, an annual cycle of observations of atmospheric properties, processes, and interactions were made while drifting with the sea ice across the central Arctic during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition from October 2019 to September 2020. An international team designed and implemented the comprehensive program to document and characterize all aspects of the Arctic atmospheric system in unprecedented detail, using a variety of approaches, and across multiple scales. These measurements were coordinated with other observational teams to explore cross-cutting and coupled interactions with the Arctic Ocean, sea ice, and ecosystem through a variety of physical and biogeochemical processes. This overview outlines the breadth and complexity of the atmospheric research program, which was organized into 4 subgroups: atmospheric state, clouds and precipitation, gases and aerosols, and energy budgets. Atmospheric variability over the annual cycle revealed important influences from a persistent large-scale winter circulation pattern, leading to some storms with pressure and winds that were outside the interquartile range of past conditions suggested by long-term reanalysis. Similarly, the MOSAiC location was warmer and wetter in summer than the reanalysis climatology, in part due to its close proximity to the sea ice edge. The comprehensiveness of the observational program for characterizing and analyzing atmospheric phenomena is demonstrated via a winter case study examining air mass transitions and a summer case study examining vertical atmospheric evolution. Overall, the MOSAiC atmospheric program successfully met its objectives and was the most comprehensive atmospheric measurement program to date conducted over the Arctic sea ice. The obtained data will support a broad range of coupled-system scientific research and provide an important foundation for advancing multiscale modeling capabilities in the Arctic.
We present an analysis of wind measurements from a series of airborne campaigns conducted to sample the wakes from two North Sea wind farm clusters, with the aim of determining the dependence of the downstream wind speed recovery on the atmospheric stability. The consequences of the stability dependence of wake length on the expected annual energy yield of wind farms in the North Sea are assessed by an engineering model. Wakes are found to extend for significantly longer downstream distances (>50 km) in stable conditions than in neutral and unstable conditions (<15 km). The parameters of one common engineering model are modified to reproduce the observed wake decay at downstream distances >30 km. More significant effects on the energy yield are expected for wind farms separated by distances <30 km, which is generally the case in the North Sea, but additional data would be required to validate the suggested parameter modifications within the engineering model. A case study is accordingly performed to show reductions in the farm efficiency downstream of a wind farm. These results emphasize not only the importance of understanding the impact of atmospheric stability on offshore wind farms but also the need to update the representation of wakes in current industry models to properly include wake-induced energy losses, especially in large offshore clusters. KEYWORDS atmospheric stability, offshore wind farm cluster, wake recovery, wind farm efficiency
In Europe, offshore wind farms have a capacity of 16 GW, with 71% installed at the North Sea. These wind farms represent an additional source of turbulence and may influence the stratification of the marine boundary layer. We present aircraft measurements and simulations showing an impact on temperature and humidity at hub height in the order of 0.5 K and 0.5gkg −1 even 60 km downwind of a wind farm cluster. We extend these simulations to explore a realistic future scenario, suggesting wakes in potential temperature and water vapor propagating more than 100 km downwind. Such impacts of wind farms are only observed in case of a strong stable stratification at rotor height, allowing wind farms to mix warmer air downward.
Abstract. Arctic boundary-layer clouds were investigated with remote sensing and in situ instruments during the Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) campaign in March and April 2007. The clouds formed in a cold air outbreak over the open Greenland Sea. Beside the predominant mixed-phase clouds pure liquid water and ice clouds were observed. Utilizing measurements of solar radiation reflected by the clouds three methods to retrieve the thermodynamic phase of the cloud are introduced and compared. Two ice indices I S and I P were obtained by analyzing the spectral pattern of the cloud top reflectance in the near infrared (1500-1800 nm wavelength) spectral range which is characterized by ice and water absorption. While I S analyzes the spectral slope of the reflectance in this wavelength range, I P utilizes a principle component analysis (PCA) of the spectral reflectance. A third ice index I A is based on the different side scattering of spherical liquid water particles and nonspherical ice crystals which was recorded in simultaneous measurements of spectral cloud albedo and reflectance.Radiative transfer simulations show that I S , I P and I A range between 5 to 80, 0 to 8 and 1 to 1.25 respectively with lowest values indicating pure liquid water clouds and highest values pure ice clouds. The spectral slope ice index I S and the PCA ice index I P are found to be strongly sensitive to the effective diameter of the ice crystals present in the cloud. Therefore, the identification of mixed-phase Correspondence to: A. Ehrlich (ehrlichA@uni-mainz.de) clouds requires a priori knowledge of the ice crystal dimension. The reflectance-albedo ice index I A is mainly dominated by the uppermost cloud layer (τ <1.5). Therefore, typical boundary-layer mixed-phase clouds with a liquid cloud top layer will be identified as pure liquid water clouds. All three methods were applied to measurements above a cloud field observed during ASTAR 2007. The comparison with independent in situ microphysical measurements shows the ability of the three approaches to identify the ice phase in Arctic boundary-layer clouds.
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