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In the coming years the geographical distribution of wind farms in Great Britain is expected to change significantly. Following the development of the "round 3" wind zones (circa 2025), most of the installed capacity will be located in large offshore wind farms. However, the impact of this change in wind-farm distribution on the characteristics of national wind generation is largely unknown. This study uses a 34-year reanalysis dataset (Modern-Era Retrospective Analysis for Research and Applications (MERRA) from National Aeronautics and Space Administration, Global Modeling and Assimilation Office (NASA-GMAO)) to produce a synthetic hourly time series of GB-aggregated wind generation based on: (1) the "current" wind farm distribution; and (2) a "future" wind farm distribution scenario. The derived data are used to estimate a climatology of extreme wind power events in Great Britain for each wind farm distribution. The impact of the changing wind farm distribution on the wind-power statistics is significant. The annual mean capacity factor increased from 32.7% for the current wind farm distribution to 39.7% for the future distribution. In addition, there are fewer periods of prolonged low generation and more periods of prolonged high generation. Finally, the frequency and magnitude

 We derive a new map of annual mean wind speeds across Greater London  Results used to assess the best location for small wind turbine installations  Small wind turbines perform better towards outskirts of Greater London  Distance from city centre is a useful parameter for siting small wind turbines  Very few sites identified which meet threshold wind speed outlined in literature To optimise the placement of small wind turbines in urban areas a detailed understanding of the 7 spatial variability of the wind resource is required. At present, due to a lack of observations, the 8 NOABL wind speed database is frequently used to estimate the wind resource at a potential site. 9However, recent work has shown that this tends to overestimate the wind speed in urban areas. This 10 paper suggests a method for adjusting the predictions of the NOABL in urban areas by considering 11 the impact of the underlying surface on a neighbourhood scale. In which, the nature of the surface is 12 characterised on a 1 km 2 resolution using an urban morphology database. 13The model was then used to estimate the variability of the annual mean wind speed across Greater 14London at a height typical of current small wind turbine installations. Initial validation of the results 15 suggests that the predicted wind speeds are considerably more accurate than the NOABL values. The 16 derived wind map therefore currently provides the best opportunity to identify the neighbourhoods 17 in Greater London at which small wind turbines yield their highest energy production. 18The results showed that the wind speed predicted across London is relatively low, exceeding 4 ms -1 19 at only 27% of the neighbourhoods in the city. Of these sites less than 10% are within 10 km of the 20 city centre, with the majority over 20 km from the city centre. Consequently, it is predicted that 21 small wind turbines tend to perform better towards the outskirts of the city, therefore for cities 22 B G L 23 useful parameter for siting small wind turbines. However, there are a number of neighbourhoods 24 close to the city centre at which the wind speed is relatively high and these sites can only been 25 identified with a detailed representation of the urban surface, such as that developed in this study. 26

A rapid decarbonisation of power systems is underway in order to limit greenhouse gas emissions and meet carbon-reduction targets. Renewable energy is a key ingredient to meet these targets; however, it is important that national power systems still maintain energy security with increasing levels of renewable penetration. The operating potential of renewable generation at times of peak demand (a critical time for power system stress) is not well understood. This study therefore uses a multidecadal dataset of national demand, wind power, and solar power generation to identify the meteorological conditions when peak demand occurs and the contribution of renewables during these events. Wintertime European peak power demand events are associated with high atmospheric pressure over Russia and Scandinavia and are accompanied by lower than average air temperatures and average wind speeds across Europe. When considering power demand extremes net of renewable power production, the associated meteorological conditions are shown to change. There is considerable spatial variability in the dates of national peak demand events and the amount of renewable generation present. Growth in renewable generation has the potential to reduce peak demands. However, these impacts are also not uniform with much larger reductions in peak demand seen in Spain than in central Europe. The reanalysis-derived energy models have allowed recent peak demand events to be put into a long-term context.

di saBatino, JunXia dou, daniel r. dreW, John M. edWards, JoaChiM fallMann, krzysztof fortuniak, JeMMa gornall, toBias groneMeier, Christos h. halios, denise hertWig, kohin hirano, alBert a. M. holtslag, zhiWen luo, gerald Mills, Makoto nakayoshi, kathy Pain, k. heinke sChlünzen, stefan sMith, lionel soulhaC, gert-Jan steeneveld, ting sun, natalie e theeuWes, david thoMson, JaMes a. voogt, helen C. Ward, zheng-tong Xie, and Jian zhong W ith the majority of people experiencing weather in urban areas, it is critical to understand cities, weather, and climate impacts. Increasing climate extremes (e.g., heat stress, air pollution, flash flooding) combined with the density of people means it is essential that city infrastructure and operations can withstand high-impact weather. Thus, there is a huge opportunity to mitigate climate change effects and provide healthier environments through design and planning to reduce the background climate and urban effects. However, our understanding of the underlying urban atmospheric processes are primarily derived from studies of separate aspects, rather than the complete, human-environment system. Air quality modeling has not been widely integrated with aerosol feedbacks on local climate, while few city-greening scenarios have tested the impacts on boundary layer pollutant dispersion or the carbon cycle. Building design guidelines have been developed without incorporating the impact of waste heat on local temperatures, which, in turn, determines building performance. Integration of such feedbacks is imperative as they define, rather than just modify, urban climate.There is an urgent need to link processes that people experience at street level (human scale) to processes at neighborhood, city, and regional scales. As these scales have traditionally been the focus for specialists in different fields, few observation and model systems cross these scales. However, understanding the interactions between these scales is critical for the design of future parametrizations ES261OCTOBER 2017 AMERICAN METEOROLOGICAL SOCIETY | and observation networks. Although models and observational methods are emerging that permit research into scale interactions [e.g., high-resolution numerical weather prediction (NWP), large-domain computational fluid dynamic (CFD) models, remote sensing, extensive sensor networks, vertical remote sensing], an integrated approach across methodologies is currently lacking.To tackle these scale interactions requires diverse skills from a wide range of research communities. This is a daunting challenge. However, improved understanding of urban atmospheric processes such as clouds and precipitation, heat transfer, and convection would enable improvements in urban system models to provide seamless hazard prediction at all time scales. Hence, an initial focus on the meteorological aspects of the research challenge may be a more manageable problem, even though the scope is still large. As such, it was identified that within the United Kingdom there is an urgent need to devel...