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An ensemble of high‐resolution regional climate model simulation data is used to examine the impacts of climate change on offshore and onshore wind energy generation in Ireland. Two Representative Concentration Pathway (RCP) scenarios (RCP 4.5 and 8.5) are analysed for the mid‐term (2041–2060) and the long‐term (2081–2100) future. Wind energy is projected to decrease (≤2%) overall in future climate scenarios. Changes are evident by mid‐century and are more pronounced by late 21st century, particularly for RCP 8.5 offshore. Seasonally, wind energy is projected to decrease by less than 6% in summer and to increase slightly in winter (up to 1.1%). The distinct changes in different parts of the power curve, presented here for the first time, show a reversed pattern of duration at certain levels of the power curve. In summer, there is an increase of low‐power and a decrease of high‐power generation, whereas during winter, there is a projected increase in the time spent at high power. This could lead to diverse consequences for system operators depending on the season. The impacts of climate change on the duration and frequency of long periods (longer than 24 h) of low‐/high‐power wind energy events in Ireland are also presented. The frequency of low‐power events is projected to increase slightly, especially during summer. Onshore and offshore events are considered separately, demonstrating the complementarity of developing both onshore and offshore wind farms for future energy systems. Regional analysis highlights the benefit of developing a geographically dispersed wind farm network incorporating different local wind conditions.

The continuing global expansion of electricity networks increases the risk of bird collisions with power lines. Several field studies have demonstrated that this risk can be reduced by marking lines with flight diverters. A before‐after control‐impact (BACI) design is currently the suggested approach for evaluating the effectiveness of these diverters and is generally assumed to give unbiased results.Using systematic flight survey data, we demonstrate that the assumptions underlying the BACI approach are frequently violated, leading to biased effectiveness estimates. We present an alternative field and statistical design in which the number of bird strike victims is directly related to bird flight intensity (“fusion design”), instead of estimating it indirectly using a control site. The presented design is validated based on simulations.We demonstrate that the presented method is unbiased and shows an approximately 3‐fold higher statistical power compared with BACI, even under ideal/unbiased data conditions, with similar field‐experimental effort. Moreover, this approach can provide a direct analysis of bird reactions/collisions, estimation of collision rates, and the possibility of conducting the required fieldwork within a single season.Our presented method can be used to standardize and improve future studies on diverter effectiveness, for example, by supporting the acquisition of a more detailed picture of species‐, diverter type‐, and habitat‐specific estimates., We compare and extend methods to analyze bird collision frequency at power lines. We present a new field and statistical approach (“fusion approach”) and demonstrate that this new method is unbiased and shows an approximately 3‐fold higher statistical power compared with previous methods.

Forecasting in wind energy is a crucial task to perform adequate wind farm flow control or to participate in the energy market. While many power forecasting methods exist, it is notoriously difficult to capture both short- and long-term variations in the wind farm system in real time. We demonstrate a data-driven real-time system identification approach to forecasting based on streaming dynamic mode decomposition methodology (sDMD). The method is capable of characterizing nonlinear, time-varying, multidimensional time series data in a computationally efficient manner. The algorithm is modified to work with data streams by adjusting the dynamic mode decomposition continuously as new data are made available. The method is applied to high-frequency SCADA data from the Lillgrund offshore wind farm. A 23.31% improvement over persistence forecasting is found for 5-min-ahead forecasts of the power output of all turbines in the wind farm. sDMD is shown to be a suitable tool for capturing short-term dynamics while adapting to long-term changes in wind speed and direction and has potential applications in real-time wind farm control.

The increased salience of environmental concerns, first and foremost global warming, is one of the key developments of contemporary Western European politics. Still, the effects of global warming issues on electoral outcomes, party competition and polarization remain poorly understood. Our article shows how the construction of wind turbines fuels conflict between its key proponents and opponents, Green and populist radical right parties. Contention over the issue contributes to the electoral success of both sides and therefore reinforces the new central divide between them. Drawing on a novel dataset, we investigate the impact of the construction of wind turbines on Alternative für Deutschland and Green party electoral success in Germany. We employ a two-way fixed effects model, where the construction of wind turbines functions as the independent variable. We show that the construction of wind turbines boosts the electoral support of both their biggest supporters and their biggest opponents. Our results have important implications for understanding contemporary political conflict in Western Europe such as the electoral rise of the Greens and the populist radical right, the importance of issue salience and the polarization of party systems.

Suburbs have the potential for future wind energy development because of their open terrain, good wind conditions, closeness to the city, wind power that can be absorbed in place, and other advantages. Therefore, accurately evaluating the wind energy resources in the suburbs is particularly important. The three‐parameter Weibull distribution can accurately describe the wind speed in the suburbs, but research on the estimation of wind power density (WPD) with it is limited. On the basis of the mathematical models of wind speed probability distribution as Rayleigh, two‐parameter Weibull, and three‐parameter Weibull, a new WPD estimation formula with three‐parameter Weibull distribution is derived using the partial integration method and by introducing incomplete gamma function to solve the transcendental integration. A field experiment platform is built, and the accuracy of the formula is verified by the measured data. The analysis of the measured data collected in recent 3 years in the suburbs of Hohhot, Inner Mongolia, concludes that the statistical characteristics of the measured wind speed and the distribution of wind energy resources are more conducive to capture the scattered and changing wind energy in the suburbs by taking the month as the time scale. Moreover, the three‐parameter Weibull distribution estimated by the least square method can be used as the local wind speed model. The new method of WPD estimation can also reduce the calculation error by approximately 21.06%. This study provides an important reference for the subsequent analysis of wind power fluctuation characteristics and wind energy development planning.

Earth system and climate models are fundamental to understanding and projecting climate change. Although they have improved significantly over the last decades, considerable biases and uncertainties in their projections still remain. A large contribution to this uncertainty stems from differences in the representation of clouds and convection (i.e., deep clouds) occurring at scales smaller than the resolved model grid resolution that is typically in the order of 100 km in the horizontal. These long-standing deficiencies in cloud parametrizations have motivated developments of high-resolution cloud- and turbulence-resolving models that can explicitly resolve clouds and convection, yet are computationally extremely expensive and can therefore only be run for a short time and/or only over a small region. However, together with the recent developments in machine learning, and especially deep learning, these simulations can be used and harvested to develop new ML-based parametrizations for clouds and convection that have the potential to eliminate some of the long-standing systematic errors in climate models. Recent research demonstrated that deep convection explicitly simulated by a cloud-resolving climate model could be correctly emulated by a deep neural network that then replaced the original parametrization in the climate model. In this chapter we describe the principal approaches for ML-based cloud parametrizations with deep neural networks and advocate that these ML algorithms need to be guided both by data and by physical knowledge. Many challenges in this new interdisciplinary field of research remain that are also discussed.

Infrasound waves travelling through atmospheric channels are affected by the conditions the encounter in their path. The shift in the backazimuth angle of a wave front detected at the reception site depends on the cross-wind it encountered. Estimating the original field from this integrated measurement is an (ill-posed) inverse problem. By using a prior, this can be converted into a Bayesian estimation problem. In this work we use the (ensemble) Kalman filter to tackle this problem. In particular, we provide an illustration of the setup and solution of the problem in a two-dimensional grid, depending on both across-track distance and height, which has not been done in previous works. We use a synthetic setup to discuss the details of the method. We show that one of the effects of along-track averaging (something done in previous studies to simplify the problem) is to overestimate the magnitudes of the analysed values, and propose that this should a source of model error. We also illustrate the process with real data corresponding to nine controlled ammunition explosions that took place in the summer of 2018. In these cases, the real infrasound waves we study seldom reach higher than 40 km in height. However, the use of covariance-based methods (e.g. the EnKF) allows for updates in higher regions where the wave did not travel, and where traditional observations are sparse. In fact, the larger impacts from observations in these cases are in the region of 40 to 60 km, agreeing with previous works. This study contributes in paving the way towards the ultimate goal of assimilating information derived from infrasound waves into operational numerical weather forecasting. More studies in quality control of the observations and proper validation of the results are urgently needed.

Understanding forecast revisions is critical for weather forecast users to determine the optimal timing for their planning decisions. A set of multi-horizon forecasts for wind speed produced by the Australian Bureau of Meteorology for 12 weather stations in eastern Australia are examined. The forecasts are examined in terms of the econometric definition of rationality and, as a robustness check, the economic value of the forecasts is also assessed using a cost–loss model. It is demonstrated that while the forecasts exhibit some of the characteristics of rational forecasts, when official testing is introduced forecast rationality is rejected at all the weather stations considered. Furthermore, the behaviour of the forecasts is shown to be very erratic over the course of the day and over forecast horizons. Although there is some evidence that the official forecasts can provide positive economic value, this metric also indicates that there is substantial room for improvement.

Airborne wind energy (AWE) is a new power generation technology that harvests wind energy at high altitudes using tethered wings. The potentially higher energy yield, combined with expected lower costs compared to traditional wind turbines (WTs), motivates interest in further developing this technology. However, commercial systems are currently unavailable to provide more detailed information on costs and power generation. This study estimates the economic value of AWE in the future electricity system, and by that indicates which cost levels are required for AWE to be competitive. A specific focus is put on the relation between AWE systems (AWESs) and WTs. For this work, ERA-5 wind data are used to compute the power generation of the wind power technologies, which is implemented in a cost-minimizing electricity system model. By forcing a certain share of the annual electricity demand to be supplied by AWESs, the marginal system value (MSV) of AWE is investigated. The MSV is found to be affected by the AWE share, the wind resource, and the temporal distribution of the AWES's electricity generation. The MSV of AWE is location- and system-dependent and ranges between 1.4 and 2.2 urn:x-wiley:we:media:we2671:we2671-math-0001 at a low share of AWE supply (0%–30%). At higher shares, the MSV drops. The power generation of WTs and AWESs are related, implying that the wind technologies present a similar power source and can be used interchangeably. Thus, the introduction of AWESs will have a low impact on the cost-optimal wind power share in the electricity system, unless an AWES cost far below the system-specific MSV is attained.

In order to integrate wave farms into the grid, the power from wave energy converters (WEC) must be forecasted. This study presents a novel data‐driven modelling (DDM) method to predict very short‐term (15 min–4 h) and short‐term (0–72 h) power generation from a WEC. The model is tested using data from an oscillating body converter. Several other methods are tested as well. These include support vector machines (SVM), neural networks (NN), and recurrent neural networks (RNN). Of these, the best is the long‐short‐term memory (LSTM) network, which is trained and updated on observed values. The experiments demonstrate both the SVM and NN forecast well. However, the proposed deep learning models predict them more accurately. The models work well over short horizons. At horizons longer than three days, accuracy deteriorates, and the models cannot fit the data well.

An idealized, three‐dimensional, numerical simulation of tropical cyclone evolution in a quiescent environment on an f‐plane is used to explore aspects of the cyclone's life cycle in the context of the rotating‐convection paradigm. In the 20‐day simulation, the vortex undergoes a life cycle including a gestation period culminating in genesis, a rapid intensification phase, a mature phase, a transient decay and re‐intensification phase, a second mature phase and a rapid decay phase. During much of the life cycle, the flow evolution is highly asymmetric, although important aspects of it can be understood within an azimuthally averaged framework, central to which are a boundary‐layer control mechanism and a new ventilation diagnostic. The boundary‐layer control mechanism provides an explanation for the gradual expansion of the inner core of the vortex. The ventilation diagnostic characterizes the ability of deep convection within a given radius to evacuate the mass of air ascending out of the boundary layer within that radius. The transient decay and re‐intensification phase is not associated with an eyewall replacement cycle, but rather with a hitherto undescribed process in which the eyewall becomes fragmented as a rainband complex forms beyond it. This process is interpreted as an interplay between the boundary layer and ventilation. The final rapid decay of the vortex results from the ever increasing difficulty of deep convection to ventilate the air exiting the boundary layer. Any unventilated air flows radially outwards in the lower troposphere and leads to spin‐down because of the approximate conservation of mean absolute angular momentum. If found in real cyclones, such transience or final decay might be erroneously attributed to ambient vertical wind shear. The results support the hypothesis that, even in a quiescent environment, isolated tropical cyclone vortices are intrinsically transient and never reach a globally steady state., A three‐dimensional, idealized numerical simulation of tropical cyclone evolution on an f‐plane is used to explore aspects of the cyclone's life cycle in the framework of the rotating‐convection paradigm. In the simulation, which lasts for 20 days, the vortex undergoes a life cycle that includes a gestation period cultimating in genesis, a rapid intensification period, a mature stage followed by a transient decay and re‐intensification stage, a second mature stage and a final rapid decay stage. The results support the hypothesis that, even in a quiescent environment on an f‐plane, isolated tropical cyclone vortices are intrinsically transient and never reach a globally steady state., U.S. Office of Naval Research http://dx.doi.org/10.13039/100000006, German Research Council

The Republic of Fiji was hit by Tropical Cyclone Winston in 2016, and housing reconstruction was enabled through government support. This study was aimed at observing the impact on housing in a rural village consisting of both modern and traditional housing types, as well as identifying the reconstruction process. The results show that the distribution of materials was delayed, and housing quality depended on local carpenters. We concluded that there was a need to train carpenters, and the reconstruction of traditional houses should be considered. This is to reflect the natural environment in tropical islands and enhance traditional building knowledge.