The weakening effect of urbanization on tropical cyclone surface winds : An observational study for Shanghai

Prevalence of hypertension in Mexican-Americans: Robert M. Palmer, University of Southern California School of Medicine, 2025 Zonal Avenue, Los Angeles, California 90033; Jean Mathison; Teresa Walsh

Assessment of wind energy potential based on Weibull and Rayleigh distribution models

Wind is the principal driver in some wind erosion models. The hourly wind speed data were generally required for precisely wind erosion modeling. In this study, a novel method to generate hourly wind speed data from daily wind statistics (daily average and maximum wind speeds together or daily average wind speed only) was established. Two typical windy locations (Lubbock and Big Spring, Texas, USA) with measured hourly wind speed data were used to validate the downscaling method. The results showed that the overall agreement between observed and simulated cumulative wind speed probability distributions appears excellent, especially for the wind speeds greater than 5 m s−1 range (erosive wind speed). The results further revealed that the values of daily average erosive wind power density (AWPD) calculated from generated wind speeds fit the counterparts computed from measured wind speeds well with high models’ efficiency (Nash-Sutcliffe coefficient).

هدف از انجام‏دادن ‏این پژوهش بررسی عملکرد پایگاه داده بازکاوی ECMWF برای توزیع زمانی-مکانی تندی باد در شرق ایران و روند آن است. به این منظور، از داده‏های بازکاوی ECMWF با تفکیک افقی 125/0×125/0 درجة قوسی استفاده شد؛ عملکرد داده‏ها با استفاده از 11 ایستگاه سینوپتیک با دورة آماری ۱۹۸۵-2015 و به‏کارگیری نمایة آماری RMSE، MBE، MAE، و R2 بررسی شد. نتایج نشان داد Interim برای بررسی تندی باد از عملکرد بالا و مناسبی برخوردار است. متوسط بلندمدت تندی باد در منطقة مورد مطالعه 56/3 m/s است؛ بیشینه و کمینة تندی باد به‏ترتیب در جولای و دسامبر اتفاق افتاده است. آرایش اصلی باد در شرق ایران شرقی و شمالی است؛ ارتباط بین تندی باد با ارتفاع معکوس و با طول جغرافیایی مستقیم و در سطح 05/0 معنی‏دار است. همچنین، ارتباط بین عرض جغرافیایی و تندی باد نشان داد که این ارتباط در ماه‏های سرد سال معکوس و در ماه‏های گرم سال مستقیم است. بررسی روند تندی باد با استفاده از آزمون من- کندال (M-K) نشان داد متوسط روند تندی باد در هفت ماه سال مثبت و در پنج ماه منفی است. همچنین، روند تندی باد در زمان آغاز (جون) باد 120روزه مثبت (۱۹۵/۰)و در زمان خاتمه (اکتبر) آن منفی (-۱۵۲/۰) است.

Introduction. Setting objectives. The environment of our life activities, its comfort and favorability largely depend on the parameters of the microclimate. One of these parameters is the wind mode. The scientific study is devoted to the topical issues of studying the wind regime of Lutsk during the last 50 years in the context of global climate changes and microclimatic features of the wind profile in the area of influence of the Beehive House. Purpose of the article. The purpose of the work is to study the wind regime in the city of Lutsk, its ecological impact, bio-ecological impact on humans and changes occurring in the context of global warming processes and restructuring of the typical course of meteorological indicators in the region. Results of the research: Changes in the wind regime of Lutsk during 1971-2020 were determined; three periods with different nature of indicators are highlighted; the peculiarities of the city's influence on the wind regime and the reverse influence of the wind on the city's microclimate were evaluated using the example of studying the wind profile in the area of influence of the Beehive House; a number of graphs, diagrams, tables were built; a number of measures are proposed that will help improve the ecological condition of the city's territory in the context of climate change. The wind regime of the city of Lutsk is formed under the influence of general circulation factors, as well as the nature of the underlying surface, the type of urban development, the topography of the city, available green areas and water bodies. In general, the wind regime of Lutsk is favorable, average wind speeds vary between 3.5-4.0 m/s, maximum gusts reach 16-20 m/s, during the year the prevailing winds are from the west, north-west, south-west directions, which is consistent with the general type of westerly transfer of air masses. The development of the city, its shape, location, configuration of individual buildings has a significant impact on the wind profile, as research in the area adjacent to the Beehive House has shown. The study of the annual dynamics of average wind speeds for 1971-2020 showed that three different periods can be distinguished in this time interval: the first period: 1971-1981, had a wind regime typical of the climatic norm, with average annual speeds of 3-3.3 m/s and increased maximum wind speeds. The second period: 1982-2000, had uneven dynamics of average annual wind speeds, their increase or decrease in individual years by 30-50% compared to the norm; the nature of the maximum values of wind speeds was also uneven. The third period: 2001-2020, which continues, is characterized by a decrease in average annual and average maximum wind speeds by 10-20% below the climatic norm, smoothed nature of the wind regime in the city. This is, in general, a favorable wind regime for human health and well-being and for carrying out economic activities of various kinds. The scientific novelty: for the first time for the city of Lutsk, a comprehensive statistical and graphic analysis of wind regime indicators and their changes over 50 years (1971 - 2020) was carried out, and the features of the impact on the microclimate of a complex architectural structure - the Beehive House, the largest residential building in Europe - were assessed. Practical significance: the results of the conducted research can be used in educational and training activities, to solve the tasks of planning the urban area, planning the construction of new industrial facilities or increasing the capacities of existing ones, building and reconstruction of quarters and microdistricts of the city of Lutsk, to assess the wind energy potential, to assess the spread of pollutants substances from generators, many of which appeared in our city this winter due to the war, from communal and industrial facilities. Keywords: wind, wind regime, average wind speed, repeatability of wind direction, microclimate of the city, environmental impact.

According to measurements at meteorological stations, average annual wind speed on Sakhalin Island varies from 2 to 9 m / c. The average annual wind speed throughout Russia is in the same range. Therefore, the study of power characteristics of the wind on Sakhalin must give general wind energy properties throughout Russia. Processing of weather stations data has been carried out using the new approach. This methodology is based on the hypothesis that the wind speed at an arbitrary interval in time is a continuously differentiable function of time V ∞ (t) . It is assumed that the function V ∞ (t) is the result of such random process, when in regular intervals Т in the identical conditions this function keeps the continuity, differentiability, and has a large number of extrema, but quantitatively changes each time a little differently. Energy properties are characterized by an average wind speed and potential of wind energy. At the same time, unlike the meteorological tradition, potential of wind energy is defined as the energy of the air stream across a unit cross-sectional area at a given time interval Т . Such a definition of wind energy potential is necessary, because the performance of wind turbines in the time interval Т is determined by the value of the wind energy potential, defined for this time interval. A number of cyclic periods of time allows us to get a sample of random variables characterizing the properties of wind energy. For this sample on the base of methods of mathematical statistics the probability density function and the root mean square deviation of these random variables are determined. As a result of statistical processing of wind speed measurements at meteorological stations of Sakhalin, empirical probability density functions of random variables “average wind speed” and “annual wind energy potential” have been identified.

风能资源作为一种无污染可再生能源，在能源结构多元化发展中具有很大的发展潜力。利用定陶1981~2015年35年国家气象观测站气象观测资料，统计分析定陶风气象要素气候变化特征，结果表明：定陶近35年年平均风速呈微弱增加趋势，气候变化倾向率为0.057 m/s/10a。春季最大，秋季最小；4月份最大，9月份最小；风速日变化明显。年平均最大风速呈减小趋势，气候变化倾向率为0.365 m/s/10a,年平均大风日数呈明显减少趋势，气候变化倾向率为−0.843d/10a。大风日数最多的是春季，最少的是冬季。以偏南风和偏北风为主(除静风外)，春、夏季以偏南风为主，冬季以偏北风为主。 Wind energy resource, as a pollution-free renewable energy in the development of diversified energy structure, has great development potential. The climatic variation characteristics of Dingtao wind speed were analyzed by using the Dingtao 35 years from 1981 to 2015 in the national metrological observation data. The results showed that the Digntao 35 years had week increase in average wind speed, and the climate change trend rate is 0.057 m/s/10a with maximum in Spring and minimum in Autumn, maximum in April and minimun in September. The annual average maximum wind speed showed a decreasing trend; the climate change tendency rate was 0.365 m/s/10a; the average annual gale days were significantly reduced; the climate change tendency rate was −0.843 d/10a. The most strong wind days is in Spring, and least in Winter, which mainly focus on southerly winds and northerly winds (except no wind), with southerly winds in spring and summer, northerly winds in winter.Wind energy resource, as a pollution-free renewable energy in the development of diversified energy structure, has great development potential. The climatic variation characteristics of Dingtao wind speed were analyzed by using the Dingtao 35 years from 1981 to 2015 in the national metrological observation data. The results showed that the Digntao 35 years had week increase in average wind speed, and the climate change trend rate is 0.057 m/s/10a with maximum in Spring and minimum in Autumn, maximum in April and minimum in September. The annual average maximum wind speed showed a decreasing trend; the climate change tendency rate was 0.365 m/s/10a; the average annual gale days were significantly reduced; the climate change tendency rate was −0.843 d/10a. The most strong wind days is in Spring, and least in Winter, which mainly focus on southerly winds and northerly winds (except no wind), with southerly winds in spring and summer, northerly winds in winter.

A detailed statistical analysis of monthly average wind speed data of monsoon period (June-September) for the year 1921-90 for 57 stations spread all over India have been reported. Probability densities, average wind speeds, standard deviations, kurtosis and skewness of wind speed frequency distribution for each station have been worked out. Histograms depicting relative frequency distribution of average wind speeds have also been prepared. It is observed that the different histograms do not exhibit any similarity among themselves indicating thereby that no single distribution is uniformly applicable for all the stations. It is also seen that the average wind speeds during monsoon period over major part of India varies from 7 to 14 kmph. Further, at most of the stations average monsoon wind speed is generally higher than average annual wind speeds. It is also noted that most of the time the wind speed exceeds 10 kmph in coastal regions of Gujarat and southern parts of the peninsular India. The information generated is of multi fold application such as (i) Identification of sites suitable for installation of Wind Energy Conversion Systems (ii) Development of Driving Rain Index and (iii) Design of buildings for creating comfortable environment indoors.

The experimental database of climatic parameters from the meteorological station of the Odesa Polytechnic National University was processed for their further use to assess the energy potential of solar and wind energy in the Odessa region. The Weibull distribution parameters were found - the shape parameter and the scale parameter, which characterizes the repeatability of wind speed. The probability of wind speed repeatability was calculated based on the found Weibull distribution parameters, which provided more objective results for determining the power of a wind turbine compared to using the average wind speed. A methodology has been developed for determining the optimal parameters and configuration of an autonomous hybrid energy supply system with renewable energy sources for an individual household based on the criterion of minimum capital investments in generating capacity. It has been investigated that seasonal differences in the energy potential of the sun and wind contribute to the combination of these renewable energy sources into a single hybrid system and create additional advantages for their operation. The dependence of the optimal configuration and parameters of the hybrid system on the energy potential of the wind in the region has been obtained. The distributions of electricity production between wind turbines and solar panels by months of the year at a given average annual wind speed, taking into account the change in wind energy potential by months, are presented. It is determined that the variable nature of solar and wind resources can be fully compensated by optimal integration of two different sources into a single energy system. It was found that under the condition of autonomous operation of the hybrid system, the excess energy potential is not always used, because it is technically difficult to ensure seasonal accumulation of electricity. The utilization factor of the installed capacity of the hybrid system was 0.58. The distributions of electricity production between wind turbines and solar panels by months of the year at a given average annual wind speed, taking into account the change in wind energy potential by months, are presented. It is determined that the variable nature of solar and wind resources can be fully compensated by optimal integration of two different sources into a single energy system. Keywords: hybrid energy supply system, renewable energy sources, wind power plant, solar photovoltaic battery, mathematical modelling, load optimization

Processing a lot of data is a very difficult and laborious task. In order to save time and ease the process, computational intelligence method is a very practical method for data processing. In the present study, the potential of wind energy in different regions of Turkey based on the hourly wind speed data in the years 2008-2017 were analysed statistically. Wind power density values have been examined mathematically and statistically and modelled using artificial intelligence methods. During the statistical analysis, maximum wind speed, average wind speed, wind power density, and standard deviation of wind speed have been determined. The cumulative Weibull function was used to determine wind power density and wind speed distribution on an annual basis using hourly wind speed data. Predictive models have been created by using machine learning algorithms which are computational intelligence method for the obtained wind power density values. Decision tree (DT) algorithm and multilayer perceptron (MLP) algorithm have been chosen as machine learning algorithms. Four different error analyses have been performed for DT and MLP estimates. In the machine algorithms used to estimate wind power values, the DT algorithm performed approximately 35% more accurate than the MLP algorithm. As a result, wind power densities for certain regions have been determined by using both mathematical model and computational intelligence methods.

The relationship between maximum wind speed and average wind speed is not linear and might be influenced by many factors. Past studies showed that the relationship can be affected by several geomorphology and surface aerodynamic factors. This study aims to reveal whether the geomorphology and surface aerodynamics factors have significant influences on the relationship between maximum and average wind speed in Indonesia. Daily maximum and average wind speed data over 2016-2022 from 56 Automatic Weather Stations (AWS) were utilized to obtain combined distribution patterns. The combined distribution patterns were clustered using the K-Means method to categorize stations with similar patterns. Six clusters of distribution patterns were found, showing some differences and similarities in geomorphological and surface aerodynamics factors. Most of geomorphological characteristics showed slight differences between the clusters, indicating that these factors might not have significant effect on the variation of the relationships between maximum and average wind. The surface aerodynamics factors This study finds that each cluster of stations has distinct geomorphological and surface aerodynamics characteristics, but currently we find no consistent pattern of parameters correlating with the relationship between maximum and average wind speed. To obtain more local and representative surface conditions, future studies may revisit the linkages using higher resolution of geomorphological and surface aerodynamics datasets.

Wind speed analysis is important for informing airport operation and safety. Many communities in the Hudson Bay and Labrador regions (Canada) are remote communities that rely heavily on aircraft for passenger and freight movement. Historical trends in average daily wind speed and maximum daily wind speed from 1971 to 2010 were examined to identify patterns of change and determine how these changes may influence aviation in six northern communities across Hudson Bay and Labrador in Canada. Significant increases in average wind speed and maximum wind speed were found for some of the months and seasons of the year for the Hudson Bay region, along with a significant decrease in those variables for the Labrador communities. Average wind speeds at multiple locations are approaching the threshold (18.5 km/h or 10 knots) when take-off and landing would be restricted to one direction. The results of this study agree with previous research that examined historical patterns for wind speed in these regions but calls into question climate change impact assessments that suggest wind speeds will continue to increase under future climatic conditions for this study area. Future research is needed to further analyse shifts in prevailing wind directions and changes in the frequency of extreme wind conditions, to better understand the potential impacts of projected climate change on this climatic variable and the implications these changes may have on applied sectors, such as aviation.

Forest fragmentation threatens forest biodiversity and ecosystem function. One of the concerns relates to increases in edge effects, which among other things affects the forest microclimate that influences the distribution and behavior of species. In Alberta, Canada, boreal anthropogenic disturbances from in situ oil exploration are increasing forest fragmentation, especially in the form of exploratory well pads and seismic lines (i.e., linear forest clearings created during the exploration phase of oil extraction). Dissection of these forests by seismic lines has the potential to change local patterns in wind and light, and thus may alter forest communities. Although alterations of these abiotic conditions are likely, the magnitude of these changes is unknown, particularly the effects of changes in the width and orientation of linear disturbances. Here we investigated changes in light and wind on seismic lines compared to that of adjacent undisturbed forests and nearby cleared openings. Specifically, we examined how seismic line characteristics (i.e., line direction, line width, and adjacent canopy height) altered local responses in these abiotic conditions. Generalized Linear Mixed Effect models predicted a 2-fold increase in average light intensity and maximum wind speeds, and a 4-fold increase in average wind speeds on seismic lines compared to adjacent forests. These changes did not approach the conditions in large openings, which compared to forests had a 3-fold increase in average light intensity, a 16-fold increase in average wind speeds, and a 4-fold increase in maximum wind speeds. Line width and orientation interacted with adjacent forest height altering the abiotic environment with wider lines having a 3-fold increase on maximum wind speed. We conclude that even localized, narrow (<10-m wide) forest disturbances associated with oil sands exploration alter forest microclimatic conditions. Recent changes in practices that reduce line width as well as promoting tree regeneration, will minimize the environmental effects of these anthropogenic disturbances.

Meteorologists keep searching and running models to provide the most accurate forecast of wind speed in addition to gaining a more detailed understanding of the wind conditions in Hungary. Wind speed and wind energy estimates, forecasts, and their verification are based on wind statistics from a longer or shorter previous period. Consequently, in addition to dynamic methods, purely statistical models also play an important role, i.e., findings that can be obtained from the statistical analysis of the existing database of measured data. The successive phases of the statistical method for producing scientific or operational information that can be extracted from measured, corrected, and stored meteorological data are generally: statistical analysis/processing, creating, verification, and application of the model, recording of the required information. The targeted information in this paper is the daily average of hourly wind speeds. The exact average of this time series can only be determined after the last measurement. To estimate this average during the day, however, the so-called sliding average model has been developed, which can be applied to any climatic element if its measured values are recorded at regular times over a certain period of time. The results presented in this paper are recommended for the preparation of the so-called "timetable", which is one of the most difficult problems for wind farm operators. This is basically the estimation of the amount of electricity produced the following day over short periods. It would be a significant help in the above if we can determine the probability of a decrease or increase in the average wind speed on the next day (and with it, the average daily wind power), or which of these two probabilities is greater. This requires an estimate of average wind speed of the next day. In addition, the results of one of our previous studies on the statistical structure of dayto-day changes in average daily wind speeds were also used. According to the results of the monthly testing of the model over a given period, the frequency of good estimates is between 80.6 % and 54.8%.

Climatic conditions for operation of wind turbines in Hungary