Modelling sea breeze climatologies and interactions on coasts in the southern North Sea: Implications for offshore wind energy

Current understanding of the behaviour of sea breezes in the offshore environment is limited but rapidly requires improvement due, not least, to the expansion of the offshore wind energy industry. Here we report on contrasting characteristics of three sea-breeze types on five coastlines around the southern North Sea from an 11 year model-simulated climatology. We present and test an identification method which distinguishes sea-breeze types which can, in principle, be adapted for other coastlines around the world. The coherence of the composite results for each type demonstrates that the method is very effective in resolving and distinguishing characteristics and features. Some features, such as jets and calm zones, are shown to influence offshore wind farm development areas, including the sites of the proposed wind farms up to 200 km offshore. A large variability in sea-breeze frequency between neighbouring coastlines of up to a factor of 3 is revealed. Additionally, there is a strong association between sea-breeze type on one coastline and that which may form coincidentally on another nearby. This association can be as high as 86% between, for example, the North Norfolk and East Norfolk coasts. We show, through associations between sea-breeze events on coastlines with contrasting orientations, that each coastline can be important for influencing the wind climate of another. Furthermore, we highlight that each sea-breeze type needs separate consideration in wind power resource assessment and that future larger turbines will be more sensitive to sea-breeze impacts

Are local wind power resources well estimated?

Planning and financing of wind power installations require very importantly accurate resource estimation in addition to a number of other considerations relating to environment and economy. Furthermore, individual wind energy installations cannot in general be seen in isolation. It is well known that the spacing of turbines in wind farms is critical for maximum power production. It is also well established that the collective effect of wind turbines in large wind farms or of several wind farms can limit the wind power extraction downwind. This has been documented by many years of production statistics. For the very large, regional sized wind farms, a number of numerical studies have pointed to additional adverse changes to the regional wind climate, most recently by the detailed studies of Adams and Keith [1]. They show that the geophysical limit to wind power production is likely to be lower than previously estimated. Although this problem is of far future concern, it has to be considered seriously. In their paper they estimate that a wind farm larger than 100 km ^2 is limited to about 1 W m ^-2 . However, a 20 km ^2 off shore farm, Horns Rev 1, has in the last five years produced 3.98 W m ^-2  [5]. In that light it is highly unlikely that the effects pointed out by [1] will pose any immediate threat to wind energy in coming decades. Today a number of well-established mesoscale and microscale models exist for estimating wind resources and design parameters and in many cases they work well. This is especially true if good local data are available for calibrating the models or for their validation. The wind energy industry is still troubled by many projects showing considerable negative discrepancies between calculated and actually experienced production numbers and operating conditions. Therefore it has been decided on a European Union level to launch a project, ‘The New European Wind Atlas’, aiming at reducing overall uncertainties in determining wind conditions. The project is structured around three areas of work, to be implemented in parallel. One of the great challenges to the project is the application of mesoscale models for wind resource calculation, which is by no means a simple matter [3]. The project will use global reanalysis data as boundary conditions. These datasets, which are time series of the large-scale meteorological situation covering decades, have been created by assimilation of measurement data from around the globe in a dynamical consistent fashion using large-scale numerical models. For wind energy, the application of the reanalysis datasets is as a long record of the large-scale wind conditions. The large-scale reanalyses are performed in only a few global weather prediction centres using models that have been developed over many years, and which are still being developed and validated and are being used in operational services. Mesoscale models are more diverse, but nowadays quite a number have a proven track record in applications such as regional weather prediction and also wind resource assessment. There are still some issues, and use of model results without proper validation may lead to gross errors. For resource assessment it is necessary to include direct validation with in situ observed wind data over sufficiently long periods. In doing so, however, the mesoscale model output must be downscaled using some microscale physical or empirical/statistical model. That downscaling process is not straightforward, and the microscale models themselves tend to disagree in some terrain types as shown by recent blind tests [4]. All these ‘technical’ details and choices, not to mention the model formulation itself, the numerical schemes used, and the effective spatial and temporal resolution, can have a significant impact on the results. These problems, as well as the problem of how uncertainties are propagated through the model chain to the calculated wind resources, are central in the work with the New European Wind Atlas. The work of [1] shows that when wind energy has been implemented on a very massive scale, it will affect the power production from entire regions and that has to be taken into account. References [1] Adams A S and Keith D W 2013 Are global wind power resource estimates overstated? Environ. Res. Lett. 8 015021 [2] 2011 A New EU Wind Energy Atlas: Proposal for an ERANET+ Project (Produced by the TPWind Secretariat) Nov. [3] Petersen E L Troen I 2012 Wind conditions and resource assessment WIREs Energy Environ. 1 206–17 [4] Bechmann A, Sørensen N N, Berg J, Mann J Rethore P-E 2011 The Bolund experiment, part II: blind comparison of microscale flow models Boundary-Layer Meteorol. 141 245–71 [5] http://www.lorc.dk/offshore-wind-farms-map/horns-rev-1 http://www.ens.d

Surface Sediments of the Pearl River Estuary (South China Sea) - Spatial Distribution of Sedimentological/Geochemical Properties and Environmental Interpretation

The Pearl River Delta (South China) is one of the densest populated regions of the world. This study aims at the investigation and interpretation of the spatial distribution of grain size parameters and geochemical parameters obtained from surface sediment samples. These samples have been taken during cruises in 2003, 2004 and 2005. Investigations of the spatial correlations of the parameters obtained reveal an approximately north-south directed trend for the majority of the parameters. The trend was removed before applying Ordinary Kriging for interpolation. The maps obtained show non-uniform distribution patterns of the sedimentological and geochemical parameters. Here e.g. the concentrations of the As, Co, Cu, Hg and Ni decrease to the more marine influenced southeastern parts and show a higher concentration in the central part and at the western shoals of the estuary.The Pearl River Delta (South China) is one of the densest populated regions of the world. This study aims at the investigation and interpretation of the spatial distribution of grain size parameters and geochemical parameters obtained from surface sediment samples. These samples have been taken during cruises in 2003, 2004 and 2005. Investigations of the spatial correlations of the parameters obtained reveal an approximately north-south directed trend for the majority of the parameters. The trend was removed before applying Ordinary Kriging for interpolation. The maps obtained show non-uniform distribution patterns of the sedimentological and geochemical parameters. Here e.g. the concentrations of the As, Co, Cu, Hg and Ni decrease to the more marine influenced southeastern parts and show a higher concentration in the central part and at the western shoals of the estuary

CONTENTS

Contents of International Journal of Offshore and Coastal Engineering Vol 1, No 2 (2017

EDITORIAL TEAM

Editorial Team of International Journal of Offshore and Coastal Engineering Vol 4, No 2 (2020

CONTENTS

Contents of International Journal of Offshore and Coastal Engineering Vol 1, No 1 (2017