Estimation of monsoon wind characteristics in India

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.

Analysis of Meteorological Conditions on Riverbed Dust Aerosol in Taiwan

Extremely high concentrations of dust particles are occasionally generated from the riverbeds of Taiwan, affecting the visibility and traffic safety of the local and nearby areas. The condition is most severe during the winter monsoon when surface wind is strong. This study analyzes the concentration of particulate matter of 10 µm or less (PM10), wind direction, wind speed, temperature, and humidity of riverbed stations adjacent to the Daan, Dajia, Dadu, Zhuoshui, and Beinan Rivers in Taiwan for a period of two years. The weather conditions that cause the high concentration of PM10 are classified into typhoon and non-typhoon types, and the latter type is further classified into three stages: ahead of front, ahead of anticyclone, and behind anticyclone. The associated meteorological influences of these weather types on high-concentration events in the riverbed are explored. The monitoring data show that the hourly PM10 concentration of the four riverbed stations exceeded 125 µg m−3 for 35–465 h per year, and the maximum PM10 in the Daan (and Dajia), and Zhuoshui Rivers was more than 800 µg m−3. Weather analysis showed that the extreme PM10 concentration on the riverbed was caused by weather types: typhoon and ahead of anticyclone, in which the peak hourly concentration reached average values of more than 600 and 400 µg m−3, respectively. The high PM10 caused by the typhoon type mainly occurred in October, with an average wind speed of 6 m s−1, high temperature of 25 °C, and mostly northeasterly winds. The ahead of anticyclone type mainly occurred in December, with an average wind speed of 5 m s−1, and northeasterly and northwesterly winds. Both weather types of riverbed events were observed during the daytime, especially at noon time, when strong wind speed, high temperature, and low relative humidity is favorable for riverbed dust generation. On the other hand, the main months of the high PM10 concentrations of the ahead of front and behind anticyclone stages are February and April. The peak PM10 concentrations of these two types of riverbed events are both about 300 µg m−3, but sporadic riverbed dust in these weather stages is mixed with Asian dust or pollution transported to the rivers through weak northwesterly and northeasterly winds. The high concentrations of these two types of riverbed events can occur at any time; but for the Dadu River, the high concentrations are often observed in the morning, when land breezes from the southeast bring local pollutants to the river.

A Review on the Building Wind Impact through On-site Monitoring in Haeundae Marine City: 2021 12th Typhoon OMAIS Case Study

Overcrowding of high-rise buildings in urban zones change the airflow pattern in the surrounding areas. This causes building wind, which adversely affects the wind environment. Building wind can generate more serious social damage under extreme weather conditions such as typhoons. In this study, to analyze the wind speed and wind speed ratio quantitatively, we installed five anemometers in Haeundae, where high-rise buildings are dense, and conducted on-site monitoring in the event of typhoon OMAIS to determine the characteristics of wind over skyscraper towers surround the other buildings. At point M-2, where the strongest wind speed was measured, the maximum average wind speed in 1 min was observed to be 28.99 m/s, which was 1.7 times stronger than that at the ocean observatory, of 17.0 m/s, at the same time. Furthermore, when the wind speed at the ocean observatory was 8.2 m/s, a strong wind speed of 24 m/s was blowing at point M-2, and the wind speed ratio compared to that at the ocean observatory was 2.92. It is judged that winds 2–3 times stronger than those at the surrounding areas can be induced under certain conditions due to the building wind effect. To verify the degree of wind speed, we introduced the Beaufort wind scale. The Beaufort numbers of wind speed data for the ocean observatory were mostly distributed from 2 to 6, and the maximum value was 8; however, for the observation point, values from 9 to 11 were observed. Through this study, it was possible to determine the characteristics of the wind environment in the area around high-rise buildings due to the building wind effect.

Analysis of the impact of geographic features, population distribution and power load on heat island effects in the metropolis of Shenzhen

Based on the air temperature data collected from automated weather stations, the urban heat island (UHI) intensity in Shenzhen metropolis is calculated and the impact of several factors, including land-sea distribution, population density, road coverage area and power load, on the UHI intensity are analyzed. The analysis shows that the land-sea distribution is the dominant factor for the UHI distribution in Shenzhen, with the climate-adjusting effect of the sea clearly reducing the UHI intensity in the east and west parts of Shenzhen. The middle part of Shenzhen is adjacent to Hong Kong and the climate-adjusting effect of the sea is weak, which leads to UHI intensity being centered around this area. The population density and road coverage area do impact the UHI in Shenzhen, with strong dependency between the UHI intensity and the two factors (p < 0.01). However, in the area with the densest roads, the UHI intensity is not high, which may be related to the high yearly-average wind speed in this area. Comparing the data from 2011 and 2010 shows strong impact of the power load on the UHI intensity in Shenzhen, and the increase of the UHI intensity in 2011 is highly likely to be due to the increase of the power load in the colder winter and the hotter summer of 2011.

Stabilization of unmanned aerial vehicle on trajectory in high wind conditions

Stationary proportional control laws have been synthesized to ensure stable motion of an unmanned aerial vehicle along a trajectory under the action of a storm wind. We give the values of the regulator coefficients for all sections of the trajectory from the starting point to the landing. Shown are the realizations of wind disturbances and the parameters of the controlled motion of the aircraft under their action. We consider the accuracy of altitude control and the error of the coordinates of the landing site. The control laws use the values of constant coefficients obtained at five points of the trajectory. Three points are used for the climb phase and one for level flight and one for descent. We took into account the wind speed as the sum of the three-dimensional turbulent component, the average horizontal component, considering the vertical shear, and discrete vertical gusts. The parameters of the Dryden shaping filters, as well as the vertical shear, are calculated for an average wind speed at a height of 6 m equal to 23.15 m / s. The speed of discrete upward gusts is 40 m/s, and downward -25 m / s. In such conditions, the unmanned aerial vehicle successfully passes the specified trajectory from the launch to the landing. For thirty realizations of flight simulation, the standard deviation of the landing site error from the wind acting was calculated.

Analisa Performa Bilah Taperless Dengan Airfoil S2091 Pada Turbin Angin Sumbu Horizontal

Utilization of wind energy is one option to produce electrical energy in the form of wind turbines. Wind energy is also renewable energy that can be utilized because of the potential for wind energy in Indonesia with an average wind speed of 2- 6 m/s. The purpose of this performance analysis is to obtain high efficiency so that the S2091 taperless blade can rotate at relatively low Indonesian wind speeds. Airfoil S2091 has an optimal Cl/Cd value to produce 500 W of power. This performance analysis uses the Blade Element Momentum (BEM) method in which the blade is divided into several elements, starting from determining the radius, chord, and twist on the blade. The assumed parameters will be simulated using Qblade v0.96 software and designing 3D blade designs using SolidWorks software. The dimensions of the taperless blade with the S2091 airfoil have a radius of 0.8 m, a chord of 0.12 m, a twist angle of 6.96o - 9.96o, and a maximum Cp value of 47% at a TSR of 4.5. At a speed of 12 m/s the maximum power generated is 998 W when the angular speed of the blade is 645 rpm and the minimum power generated is 95 W. Then the average power generated is 640.94 W. The results of field tests have a maximum charging power of 138 .46 W and an average charging of 14.13 W. Then the power obtained is 257.80 Wh. From these data, the efficiency of the blade system is 30%–40% and the efficiency of field testing is 34.16%.

Normalized anomaly patterns of meteorological elements: A case study of Northeast monsoon 2002 and 2004

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Calculation of Weibull Distribution parameters at low wind speed and performance analysis

In this study, the compatibility of the real wind energy potential to the estimated wind energy potential by Weibull Distribution Function (WDF) of a region with low average wind speed potential was examined. The main purpose of this study is to examine the performance of six different methods used to find the coefficients of the WDF and to determine the best performing method for selected region. In this study seven-year hourly wind speed data obtained from the general directorate of meteorology of this region was used. The root mean square error (RMSE) statistical indicator was used to compare the efficiency of all used methods. Another main purpose of this study is to observe the how the performance of the used methods changes over the years. The obtained results showed that the performances of the used methods showed slight changes over the years, but when evaluated in general, it was observed that all method showed acceptable performance. Based on the obtained results, when the seven-year data is evaluated in this selected region, it can be said that the MM method shows the best performance.

Algorithm design for wind prediction in Berakit Bay, Bintan Island using Long Short-Term Memory (LSTM) method

Wind speed is a crucial parameter alongside coastal areas, especially Indonesia. Above average wind speed can cause harmful effects on human activities. This study uses wind speed data from Berakit Bay, Bintan Island is a potential location for coastal community settlement, fisheries, and tourist activities. The wind parameter then predicted using the Long Short-Term Memory or LSTM algorithm. This algorithm is able to study long-term dependencies by converting simple nervous system designs into specialized blocks containing cells. It is suitable to be applied to long-term wind predictions where the wind speed at this time is very influential with the wind speed in the future. In preparing the LSTM, the data preprocessing and the architecture used will determine the prediction results. In this study, four different architectures were made in order to determine the most optimal architecture. The results show that the LSTM architecture is able to obtain a relatively good RMSE value of 1.87 and an accuracy of 39.40% with the use of two LSTM layers, 256 units in the first layer and 128 in the second layer. The LSTM algorithm in predicting wind can also be applied to other areas in Indonesia.

Temporal and spatial variation characteristics of temperature and its impact on grain production in Northeast China in recent 50 years

Climate warming has a great impact on grain production in northeast China, but there are few studies on the temporal and spatial variation characteristics of annual Tmean (mean temperature), the impact of meteorological factors on Tmean, and the impact of Tmean increase on grain production in northeast China. This study found that annual Tmean decreased from southeast to northwest in Northeast China, and there were regional differences in spatial distribution. The annual Tmean isoline in Northeast China moves obviously from southeast to northwest. The annual warming trend of Tmean was significant from 1971 to 2000, and moderated from 2001 to 2020. In recent 50 years, Tmean had obvious periodic changes. In the mid-late 1980s, annual Tmean had a sudden warming change, and since then it has been rising continuously. Sunshine hours, average wind speed, evaporation and average air pressure had a very significant correlation with Tmean. In conclusion, the climate change in northeast China in the past 50 years has an obvious warming and drying trend, and there are regional differences in the warming and drying. The warming and drying climate has brought challenges to agricultural production and food security in Northeast China. However, the negative effects of grain production reduction caused by warming and drying climate can be avoided to a certain extent if we deal with it properly.