A Refined Study of Atmospheric Wind Properties in the Beijing Urban Area Based on a 325 m Meteorological Tower

The urban atmospheric boundary layer (UABL) is complex due to the heterogeneous underlying city surface. The nine anemometers installed at different heights along the 325 m meteorological tower provide an opportunity to carry out a refined study of wind properties in the UABL in central Beijing, China. Based on the recent 5-year high-resolution measured data, in total, 229,488 10-min length segments of wind records related to each anemometer are reliable for further analyses. Accordingly, the statistical properties of the wind speed and direction are first analyzed to present the local wind climate in a comprehensive way. Moreover, the pattern of the wind profiles related to two typical synoptic intense events are illustrated in order to give a preliminary perspective, then the statistical properties corresponding to a series of intense windstorms are described. Here, the deviations in the wind direction occur between 200 m and 280 m of the atmosphere, which might be due to the existence of an Ekman spiral; besides this, the laws of wind profiles based on open terrain are not suitable for the UABL, and the aerodynamic characteristic parameters of the UABL based on vertical stratified structures have to be considered. The results contribute to the establishment of revised models for the wind profile and are useful for the further understanding of the structure of UABL wind.

Ten years of the Wind Atlas of South Africa: Final results from WASA 3

<p>We present the evolution of the methods used to create and validate the various numerical wind atlases during the past ten years of the Wind Atlas for South Africa (WASA) project. In WASA 3, we improved on the previous numerical wind atlases by:</p><ul><li>Creating an ensemble of 2-year simulations to find the optimal set of parameterisations and surface conditions for the wind climate of South Africa.</li> <li>Using a new method of generalisation and downscaling of the WRF-derived wind climate using the PyWAsP engine.</li> <li>Producing the most extensive to date wind climatology for South Africa, 30 years (1990–2019) simulation covering all South Africa at 3.33 km × 3.33 km spatial resolution and 30 minutes time output.</li> </ul><p>We will discuss these three areas and their improvements to the wind atlas' quality. The WASA 3 wind atlas' final error statistics show that the new WRF + PyWAsP method has a MAPE of 11.8% and 3.5% for the long-term mean power density and mean wind speed, respectively. These statistics are improved from those in WASA 1 and WASA 2.</p><p>When disregarding the two masts (WM09 and WM11) located in highly complex terrain, where the methodology was never designed, the use of the WRF and WRF + PyWAsP downscaling narrows the error distributions for both long-term wind speed and power density compared to the global reanalysis, ERA5.</p><p>The validated numerical wind atlas has further been used to model the wind resources of the entire land area of South Africa using the microscale WAsP model. Raster data exist with a horizontal resolution of 250 meters and three levels of 50, 100 and 150 meters a.g.l. of mean wind speed, power density, air density, Weibull <em>A </em>and<em> k </em>parameters, and ruggedness index.  These data sets and the WRF dataset will be made available in the public domain at the end of the project. Data sets for other heights above the ground and offshore can easily be added later.</p>

Extreme gust atlas over South Africa

<p>An atlas of the 50-year gust wind at a resolution of 3 s is calculated over South Africa, at a spatial resolution of 3.3 km at several heights, including 10 m and 60 m where measurements are available.</p><p>In developing the atlas, first, 30-year wind climate (1990 - 2019) is simulated using the Weather Research and Forecasting (WRF) model. The WRF model was initialized and forced with the ERA5 data, with three nested domains and the innermost one, covering the whole country, has a spatial resolution of 3.3 km. The model outputs include the wind time series at several heights (50 m, 100 m and 200 m) every 30 minutes. The 50-year 30-min winds at several heights are then obtained by application of a suitable extreme value distribution. Afterwards, the Kaimal turbulence model is applied, in connection with an assumption of Gaussian process for the time series in the time scale 30 min to 3 s, to obtain the corresponding 3 s gust value to the 30-min values of the 50-year winds.</p><p>There is a prevalence of a variety of strong wind events in South Africa, including mid-latitude cyclones, fronts and thunderstorms. The different physical mechanisms have different levels of challenges to the simple modeling approaches applied above. For more than 100 measurement stations, the 50-year gust values have also been calculated, mostly at 10 m, some at 60 m. They are used to validate the modeled values and identify regions and areas where our meso-to-turbulence modeling needs improvement or adjustment to eventually produce a verified extreme gust atlas.</p>

A Multi-Point Meso–Micro Downscaling Method Including Atmospheric Stratification

In wind energy site assessment, one major challenge is to represent both the local characteristics as well as general representation of the wind climate on site. Micro-scale models (e.g., Reynolds-Averaged-Navier-Stokes (RANS)) excel in the former, while meso-scale models (e.g., Weather Research and Forecasting (WRF)) in the latter. This paper presents a fast approach for meso–micro downscaling to an industry-applicable computational fluid dynamics (CFD) modeling framework. The model independent postprocessing tool chain is applied using the New European Wind Atlas (NEWA) on the meso-scale and THETA on the micro-scale side. We adapt on a previously developed methodology and extend it using a micro-scale model including stratification. We compare a single- and multi-point downscaling in critical flow situations and proof the concept on long-term mast data at Rödeser Berg in central Germany. In the longterm analysis, in respect to the pure meso-scale results, the statistical bias can be reduced up to 45% with a single-point downscaling and up to 107% (overcorrection of 7%) with a multi-point downscaling. We conclude that single-point downscaling is vital to combine meso-scale wind climate and micro-scale accuracy. The multi-point downscaling is further capable to include wind shear or veer from the meso-scale model into the downscaled velocity field. This adds both, accuracy and robustness, by minimal computational cost. The new introduction of stratification in the micro-scale model provides a marginal difference for the selected stability conditions, but gives a prospect on handling stratification in wind energy site assessment for future applications.

Wind climate of the Melbourne metropolitan area

This paper describes a probabilistic analysis of data recorded by the Bureau of Meteorology (BoM) for the wind climate of the Melbourne metropolitan area. It is based on 10-minute average wind data from four automatic weather stations (AWS) ‒ at Melbourne and Essendon airports, Fawkner Beacon in Port Phillip Bay, and Moorabbin Airport. Corrections to the data were made to adjust to standard terrain conditions and height. For the land stations, these were based on estimates of the surface roughness length at each site as a function of wind direction, making use of recorded gust factors. For the Fawkner Beacon, which is completely surrounded by open water, the surface roughness length is a function of mean wind speed, and the Charnock relationship was used in determining the corrections. For each station the terrain-corrected wind data were fitted with Weibull probability distributions, as an all-direction group and for sixteen direction sectors. Directional probabilities were also determined. The parameters of the all-direction Weibull distributions are very similar for all four stations, but there are differences in directional probabilities for some directions, with a geographic trend from north to south in the region being apparent. Some possible explanations based on the general topography are given.

Design of the A1-M1 link road extradosed bridge in Mauritius

<p>The A1-M1 Link Road is a 1 km highway project, currently under construction in Mauritius. The project will cross the Grand River North West Valley, a 90 m deep gorge, with a 3-span extradosed bridge.</p><p>The island of Mauritius is subject to major cyclonic winds and the gorge being crossed needed to be adequately accounted for when assessing the wind effect on the bridge. A detailed wind climate study of the project site was conducted to derive wind buffeting loads for the design of the bridge.</p><p>In addition, particular geotechnical stability issues encountered at the cliffs on either side of the gorge, dictated a non-optimum span distribution which required a complex arrangement of temporary stay cables.</p><p>When complete, the bridge will be a key component of the A1-M1 Link Road Project and will link the existing A1 Road and M1 Motorway, improving connectivity on the West Coast of the Island.</p>

Climate Change Induced Uncertainty of Wind Energy Potential for the Azov and Black Seas Coastal Zone

The paper deals with assessing an impact of wind climate change on the wind energy potential of the Azov and Black Sea coast region. A lower estimate of operating time for wind power installation and a potential annual energy output for the region are given for the case of Vestas V117-4.2MW. Calculation has been performed of a long-term mean wind speed for two adjacent climatic periods (1954–1983 and 1984–2013) based on data from meteorological stations of the Black and Azov Sea region. The results show a decrease in wind speed at all meteorological stations except for Novorossiysk. The wind climate change is confirmed by comparing two adjoined 30-year periods and by estimating linear trends of the mean annual wind speed for the period 1954–2013, which are negative and significant for almost all meteorological stations in the region (α = 1 %). The trend values were estimated by the nonparametric method of robust linear smoothing using the Theil – Sen function. In the present study, the uncertainty of wind energy resource induced by a gradual wind climate change is estimated for perspective planning of this branch of energy sector. Despite the observed trends in the wind regime, average wind speeds in the Azov and Black Sea region are sufficient for planning the location of wind power plants.