Monthly Wind Research Articles

Effect of SST in the Northwest Indian Ocean on Synoptic Eddies over the South China Sea-Philippine Sea in June

Synoptic eddies (with a period of two to eight days) are active in the South China Sea-Philippine Sea (SCS-PS) and control weather variations. In addition, the intensity and frequency of synoptic eddies may change along with variations in sea surface temperatures (SST). This paper presented the influence of SST in the northwest Indian Ocean on synoptic eddies in the lower troposphere over the SCS-PS in June. Our statistical analysis showed a significant negative correlation between the SST in the northwest Indian Ocean and the synoptic scale eddy kinetic energy (EKE) in the SCS-PS. By analyzing the EKE budget of synoptic eddies, we found that the variation in the synoptic scale EKE over the SCS-PS is mainly due to the change in the monthly zonal wind gradient, which affects the barotropic energy conversion between the monthly mean flow and the synoptic eddies. Additionally, the northwest Indian Ocean SST modulates the monthly flow over the SCS-PS by alternating the strength of the Walker circulation in the west Pacific and Indian Ocean. Finally, the influence of SST in the northwest Indian Ocean on EKE in the SCS-PS was reproduced using the simplified atmospheric general circulation model, SPEEDY.

Accurate and efficient forecasted wind energy using selected temporal metrological variables and wind direction

The aim of this work is to find the most efficient and suitable input features to be selected for forecasting monthly wind energy accurately. Machine learning is employed for a modular pipelined neural network, composed of time-delayed and feedforward networks with features of metrological variables such as atmospheric temperature, humidity, wind direction, and wind speed frequency distribution parameters. Logged data over a year’s period at a UAE site are analyzed on daily and monthly bases depending on their variation characteristics, in which standard Weibull probability distribution function is used for the feedforward neural network together with wind direction data, while daily average ambient temperature and humidity are attempted for the composite time delay networks. Different network abstractions of input features are compared, and it is found that wind direction data offer a better wind speed forecast. Wind energy is calculated based on monthly forecasting. A detailed adaptive probabilistic analysis is conducted to predict thresholds in variations of the forecast analysis. Error estimation tools are performed for adopting this method

Wadi Halfa average monthly wind speeds in knots, 2006-2016

Wadi Halfa average monthly wind speeds in knots, 2006-2016

Monthly Wind Power Forecasting: Integrated Model Based on Grey Model and Machine Learning

Wind power generation has been developed rapidly due to rising global interest in renewable clean energy sources. Accurate prediction of the potential amount of such energy is of great significance to energy development. As wind changes greatly by season, time series analysis is considered as a natural approach to characterize the seasonal fluctuation and exponential growth. In this paper, a dual integrated hybrid model is presented by using random forest (RF) to incorporate the extreme gradient boosting (XGB) with empirical mode decomposition (EMD) and a fractional order accumulation seasonal grey model (FSGM). For seasonal fluctuation in vertical dimension processing, the time series is decomposed into high and low frequency components. Then, high and low frequency components are predicted by XGB and extreme learning machine (ELM), respectively. For the exponential growth in horizontal dimension processing, the FSGM is applied in the same month in different years. Consequently, the proposed model can not only be used to capture the exponential growth trend but also investigate the complex high-frequency variation. To validate the model, it is applied to analyze the characteristics of wind power time series for China from 2010 to 2020, and the analysis results from the model are compared with popularly known models; the results illustrate that the proposed model is superior to other models in examining the characteristics of the wind power time series.

Forecasting Monthly Wind Energy Using an Alternative Machine Training Method with Curve Fitting and Temporal Error Extraction Algorithm

The aim of this research was to forecast monthly wind energy based on wind speed measurements that have been logged over a one-year period. The curve type fitting of five similar probability distribution functions (PDF, pdf), namely Weibull, Exponential, Rayleigh, Gamma, and Lognormal, were investigated for selecting the best machine learning (ML) trained ones since it is not always possible to choose one unique distribution function for describing all wind speed regimes. An ML procedural algorithm was proposed using a monthly forecast-error extraction method, in which the annual model is tested for each month, with the temporal errors between target and measured values being extracted. The error pattern of wind speed was analyzed with different error estimation methods, such as average, moving average, trend, and trained prediction, for adjusting the intended following month’s forecast. Consequently, an energy analysis was performed with effects due to probable variations in the selected Lognormal distribution parameters, according to their joint Gaussian probability function. Error estimation of the implemented method was carried out to predict its accuracy. A comparison procedure was performed and was found to be in line with the conducted Markov series analysis.

Dynamic Modelling of Water and Wind Erosion in Australia over the Past Two Decades

Soil erosion caused by water and wind is a complicated natural process that has been accelerated by human activity. It results in increasing areas of land degradation, which further threaten the productive potential of landscapes. Consistent and continuous erosion monitoring will help identify the location, magnitude, and trends of soil erosion. This information can then be used to evaluate the impact of land management practices and inform programs that aim to improve soil conditions. In this study, we applied the Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ) to simulate water and wind erosion dynamics. With the emerging earth observation big data, we estimated the monthly and annual water erosion (with a resolution of 90 m) and wind erosion (at 1 km) from 2001 to 2020. We evaluated the performance of three gridded precipitation products (SILO, GPM, and TRMM) for monthly rainfall erosivity estimation using ground-based rainfall. For model validation, water erosion products were compared with existing products and wind erosion results were verified with observations. The datasets we developed are particularly useful for identifying finer-scale erosion dynamics, where more sustainable land management practices should be encouraged.

Background risk and risk-taking – evidence from the field

AbstractMost decisions involving risk are not taken in isolation. In addition to the risk from that decision, other independent, so-called ‘background’ risks, are considered. Our research adds to the growing evidence that this background risk influences risk-taking. We report results from a repeated lab-in-the-field investment task with Senegalese fishers in the presence of background risk related to their fishing income and their health. Our measure of background risk is the monthly wind condition. Without controls, we find that fishers act on average intemperately. Adding controls, we find that the impact of background risk on risk-taking—measured as the investment in the investment task—depends on the boat size of the fishers. When dividing the sample according to wealth, we find temperate behavior for the relatively poorer group and intemperate behavior that depends on boat lengths for the relatively richer group. Our results show the interrelations between background risk and context factors.

Exploration of Atmosphere‐Only Model Deficiencies in Reproducing the 1992–2011 Pacific Trade Wind Acceleration

AbstractA robust simulation of tropical Pacific Ocean decadal‐scale zonal wind stress trends can increase the confidence in projections of global surface temperature and regional sea level rise, yet coupled general circulation models simulate weaker trends than observed. When forced with observed sea surface temperatures, the atmospheric component of these models still simulates a weaker zonal wind stress trend during 1992–2008 (and 1992–2011) than observed. Yet, this decadal wind trend bias is not evident at the 850 hPa level. We show that the modelled wind trend bias is related to deficiencies in monthly wind stress variability. Furthermore, the strength of the connectivity between the surface and 850 hPa was also an indicator of the wind stress trend. Models that simulated a more realistic wind stress trend tend to simulate a stronger Pacific zonal sea level pressure gradient intensification than observed and a South Pacific Convergence Zone shift like that observed.

Estimation of wind speed distribution with time window and new kernel function

Wind speed distribution analysis is important for selecting the potential wind farm and improving wind energy utilization efficiency. However, due to the randomness, intermittence, and chaos of wind speed, it is difficult to accurately estimate its probability distribution. Many studies use specific probability density function to estimate wind speed distribution, but only a few analyze the changes of wind speed distribution in the same region. The purpose of this study is to improve the accuracy of wind speed distribution estimation and analyze the changes in wind speed distribution under different time windows. Three new kernel functions are proposed to improve the accuracy of distribution estimation. A new point-to-point comparison method is proposed to evaluate the goodness-of-fit of wind distribution. A new time window analysis method is applied to analyze the monthly, quarterly, and semiannual wind speed distribution. The results show that (a) under different time windows, the wind speed distribution in Hexi Corridor is different; (b) the performance of kernel function is affected by its peak value and shape; and (c) one of the three new kernel functions has the smallest error, in which mean square errors in monthly, quarterly, and semiannual time windows are 0.0057, 0.0061, and 0.0056, respectively.

Wind Predictions in the Lower Stratosphere: State of the Art and Application of the COSMO Limited Area Model

In the last few decades there has been increasing interest in the commercial usage of the stratosphere, especially for Earth observation systems. Stratospheric platforms allow Earth monitoring at a regional scale with persistency toward a limited area. For this reason, accurate meteorological forecasts are needed in order to guarantee stationarity. The main aim of this work is to provide a review of wind prediction techniques in the stratosphere, achieved by the most popular global models, such as ECMWF IFS, NCEP GFS and ICON. Then, the capabilities of the COSMO limited area model to reproduce the wind speed in the stratosphere are evaluated considering a model configuration with very high resolution (about 1 km) over a domain located in Southern Italy, assuming the radio sounding data at Pratica di Mare airport as the reference. Vertical profiles were analyzed for selected days, highlighting good performances, though improvements can be achieved by adopting a fifth-order interpolation of the model data. Finally, monthly wind speed time series for selected heights were post-processed by means of fast Fourier transform, revealing the existence of main frequencies and the presence of a scaling regime and a power law of the form f−β over a broad range of time scales, in the Fourier space. The exponent spectral β is close to the exact 5/3 Kolmogorov value for all the datasets.

Contribution of complementary operation in adapting to climate change impacts on a large-scale wind–solar–hydro system: A case study in the Yalong River Basin, China

Operation flexibility of hydropower stations and regulation ability of reservoirs can complement intermittent wind and photovoltaic power to form a stable wind–solar–hydro complementary system (WSHCS). It is acknowledged that alteration of wind, solar, and water resources by climate change might degrade power generation of WSHCSs; however, comprehensive evaluation of the potential impact of climate change on the energy production, reliability, and power curtailment of WSHCSs remains lacking. Furthermore, quantification of the contribution of complementary operation of a WSHCS in adapting to climate change impacts represents another vital knowledge gap. To address these problems, this study constructed a top-down model chain by integrating global climate models, hydrological models, and energy system operation models. The large-scale WSHCS in the Yalong River Basin (China) was considered as a case study. Results indicated substantial complementarity among the variation of monthly streamflow, wind speed, and solar radiation under a changing climate. The total energy production and reliability of the WSHCS could be guaranteed under most future climate scenarios, while power curtailment risk would increase. Although climate change impacts dominate change in system operation performance, complementary operation can contribute a positive effect to a certain extent, especially for higher reliability and lower power curtailment. The findings indicate the effectiveness of the complementary operation in adapting to climate change impacts on the Yalong River WSHCS. The major novelty of this study is quantification of the contribution of complementary operation in adapting to climate change impacts on WSHCSs, which provides valuable insight regarding evaluation of complementary systems.

Modeling and Forecasting Selected Meteorological Parameters for the Environmental Awareness in Sub-Sahel West Africa Stations

The monthly air temperature, rainfall, air pressure, and wind speed direction for the environmental time series recorded between January 1, 1980 and December 31, 2020 in six African stations from different climatic zones were modeled and forecasted. In the forecasting, Augmented Dickey Fuller test, ARIMA models, Auto correlation Function (ACF) and Partial Autocorrelation Function (PACF) were used. Result showed that in most of the fitted models, the Moving Average terms both seasonal and non- seasonal were also significant (p<0.05) indicating that the previous day value of the stochastic term also had a significant effect on the present value of meteorological parameters in the environment. It was observed that in all the fitted models except for wind direction in Conakry and rainfall in Abidjan have all their Autoregressive term of order 1 significant (p<0.05) which implies that previous day value of these meteorological parameter had a significant effect on the present day value of the parameters. Therefore, the forecast model indicates that maximum temperature are expected in February, March, April, and June while minimum temperatures in January, August, December. Although, the selected models cannot forecast the precise air temperature, this can also provide information that can be of help to create tactics for appropriate preparation of farming which can be used as tools for effective envi­ronmental preparation and policymaking.

Аssessment of the influence of physical factors of the external environment on the heat feeling of a human in the conditions of Kamchatka

OBJECTIVE: Characterize the climate impact on the population of Petropavlovsk-Kamchatsky regarding warmth sensation in different periods of the year.MATERIALS AND METHODS: The length of a warm and cold period of the year was determined. Based on mean monthly temperatures, relative humidity, wind speed, maximum wind and minimum temperature, taken from the website of weather condition records in Petropavlovsk-Kamchatsky for 2010–2020, the equivalent-effective temperature (EET) has been calculated that allows to estimate the warmth sensation of a man, stripped to the waist.RESULTS AND DISCUSSION: According to the average temperature, the warm period is in June-September, the cold one is 8 months. As for EET, thermal comfort is in July-August; it’s cool in June and September; it’s cool/moderately cool in October; it’s moderately cool in April-May and November; it’s very chilly in December-March. Speaking about minimum temperature and maximum wind, it’s warm in August, warm/cold in September; 10 months is a cold period of the year. By comparing EET, it’s cool in July and August; it’s moderately cool in May, June, September and October; it’s very chilly in April and November, moderately cold in December and March and cold in January. In February conditions are moderately cold/cold.CONCLUSION: Amidst high humidity the wind produces a meaningful effect on a bioclimatic discomfort.

Analysis of trends of meteoclimatic characteristics at Enderby Land, East Antarctica based on land observations data and reanalysis

The article studies the presence and possible direction of climate change in Enderby Land, East Antarctica, where from 1963 to 1999, the only meteorological station of the WMO network operated at the Molodezhnaya research station (now the summer field base of the RAE), and now an automatic meteorological station has been installed. The trends in surface air temperature, wind speed and direction for the period from the mid-1960s to the present have been analyzed. As initial data, we used the results of ground-based observations at the Molodezhnaya station, as well as reanalysis datasets, which made it possible to complete the instrumental series and evaluate the dynamics of temperature and wind characteristics for the last decades. Monthly values of climatic parameters were used as initial values, but for wind characteristics presented as the V and U components of the speed vector, single values were taken, according to which the average monthly wind speeds and wind distributions in directions were estimated. From different reanalysis systems, by comparison with ground-based observations, series were selected that showed the greatest similarity in the dynamics of the climatic characteristics studied. Good agreement was found between the temperature series of observations at the Molodezhnaya meteorological station and the datasets UDEL_AirT, ERA5, and GISS. The correlation between the wind observation series is less strong; ERA5 reanalysis data shows the wind speed dynamics slightly better. According to the analysis of the long-term dynamics of average annual and monthly temperatures in the region of Enderby Land, the warming trend has not been statistically confirmed, however, a progressive increase in the number of months of the year with positive temperature dynamics has been noted. Based on the analysis of wind characteristics, it is shown that only an increase in average annual wind speeds of 0,1–0,3 m/s/10 years is statistically significant.

Assessment of wind energy potential using Poisson distribution model - A field study in India

Wind power is the cheapest clean renewable power technologies for power generation. It is observed that the generation of electrical energy from wind using a wind turbine generator (WTG) system is a tedious task as it requires a critical study of wind speed parameters. In this study, Poisson distribution is used for evaluating the wind power potential in Chalkewadi, Satara, India. Poisson distribution is utilized and parameters are calculated using monthly average wind speed data. The wind speed data has been collected at the height of 50 m over a period of three-years 2016-2018. Variations in annual and monthly wind speed are analyzed and power densities are calculated. The results show that the largest values of wind power potential occur from June to September in Chalkewadi, Satara and lowest from November to February. In wind-based electrical systems, it is necessary to gain the characteristics of wind speed as it is used to design the WTG system which affects the economics i.e. cost of generation. A comparative analysis is done which shows that the maximum available power density at Chalkewadi, Satara is 2841 W/m2. This study also shows that for designing WTG it is required to take the optimum value of the capacity factor.

Environmental Factors Affecting Near-Surface Oxygen Content Vary in Typical Regions of the Qinghai-Tibet Plateau

Hypoxia threatens the health of people in highland regions around the world. Recent studies have shown that altitude has a significant effect on near-surface oxygen content at broad geographic scales, and whether this effect is manifested at the landscape scale, particularly on the Qinghai-Tibetan Plateau (QTP) in China, has not been documented. We investigated the relationship between altitude, vegetation (Net Primary Productivity, NPP; Leaf Area Index, LAI; Fractional Vegetation Cover, FVC; Length of the Growing Season, LOS and Vegetation Type), climate variables (Mean Monthly Temperature, MMT; Mean Monthly Precipitation, MMP and Wind Speed, WS), and near-surface oxygen content in five typical regions of the QTP, including the Qilian Mountains (QLM), the Three-River Headwater Area (THA), Ngari Prefecture (NAP), Hengduan Mountains (HDM) and Yajiang Valley (YJV). The results showed that 1) altitude, NPP, FVC, LOS, MMP and MMT were important variables for predicting near-surface oxygen content; 2) the local-scale relationship between altitude and oxygen content exhibited negative or no correlations across broad regions; and 3) oxygen content exhibited a great deal of spatial heterogeneity, with altitude being the main affecting factor, followed by vegetation. Different drivers of near-surface oxygen content should be incorporated into Earth system models to reduce uncertainty in predicting oxygen dynamics and its potential feedback to global warming.

An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction

Accurate prediction of wind speed is of great significance to the stable operation of wind power equipment. In this study, a hybrid deep learning model based on convolutional neural network (CNN), Bi-directional long short-term memory (BiLSTM), improved sine cosine algorithm (ISCA) and time-varying filter based empirical mode decomposition (TVFEMD) is proposed for wind speed prediction. Firstly, the original wind speed data is decomposed into intrinsic mode functions (IMFs) by TVFEMD to improve the data stability. Then, the importance of each decomposed subcomponent is analyzed using random forest (RF). Thirdly, CNN-BiLSTM is employed to predict the wind speed. And, an improved sine and cosine algorithm (ISCA) is utilized to optimize the model parameters BiLSTM. Finally, the forecasting results of each sub-model are combined to get the final prediction results. In this study, the proposed model is utilized to four monthly wind speed data sets, and different comparison models are established. The experimental results of this study show that TVFEMD and RF can process data more effectively and improve the prediction accuracy. ISCA can optimize the parameters of BiLSTM model and improve the prediction performance. The proposed model in this study can obtain good prediction results on all data sets.

Characteristics of Wind Resources and Post-Project Evaluation of Wind Farms in Coastal Areas of Zhejiang

As the onshore wind farm technology matures, offshore wind energy has attracted increasing attention. Zhejiang has coastal areas with massive potential for wind resources because of its geographical location. Therefore, understanding the wind resources in these areas can lay a foundation for future development and utilization. On this basis, this study used the measured wind field data of a wind farm along the coast of Zhejiang from March 2014 to February 2015 and from March 2016 to February 2018 to investigate and compare the characteristics of wind energy resources, including average wind speed, Weibull shape and scale factors, wind direction variation, and wind energy density. Then, the capacity coefficient of a wind turbine predicted using the wind farm data was compared with the actual capacity coefficients of two wind turbines in the wind farm in 2019. Results revealed the following observations: The overall variations in the evaluation indicators followed clear patterns over the 3 years. For example, the main wind direction in the same season was the same, and the variations in the monthly average wind speed, the monthly wind power density, and the theoretical capacity factors were highly similar. The time-series data indicated that the difference in the indicators between summer and autumn was significantly larger than that between other seasons, with the maximum difference in monthly average wind speed of 1.46 times and the maximum difference in monthly wind power density of 1.5 times. The comparison results of the capacity coefficient showed that the theoretical and actual capacity coefficients were extremely close when the monthly average wind speed was less than 6 m/s, with the average difference being less than 9%. When the monthly average wind speed was greater than 6 m/s, the proximity between the theoretical and actual capacity coefficients was reduced, with an average difference of more than 9% and a maximum value of 28%. In general, the overall characteristics of wind resources in coastal areas of Zhejiang exhibited similar trends but fluctuated considerably in some months. Wind energy forecasts had significant discrepancies from the actual operation indicators of the wind farm when the wind speed was high.

ASSESSMENT OF WIND ENERGY POTENTIAL FOR ACCRA, GHANA USING TWO PARAMETER WEIBULL DISTRIBUTION FUNCTION

This study investigates the predictive ability of two-parameter Weibull distribution function for Accra, Ghana using fifteen years (2002 – 2016) meteorological parameter of monthly mean wind speed data. The monthly and annual averaged wind speed was estimated to be . The annual mean values of the maximum energy – carrying wind speed and most probable wind speed were found to be and respectively. The two parameters of the Weibull statistics found in this study for Accra were within the range for the shape parameters and for the scale parameters. The results of the wind power density indicated that the location has a good prospect for wind power generation with the highest value of found in September, 2006. The linear relationship between the monthly mean wind power density and mean wind speed shows that a perfect correlation with coefficient of correlation of 99.3 % exists between them. The results obtained from this study revealed that the Weibull function was found appropriate for analyzing measured wind speed data and in predicting the wind power density for the region under investigation. Therefore, Accra has an excellent prospect for wind power generation

A Comparative Study of Wind Characteristics Between South-Western and South-Eastern Thailand Using Different Wind Turbine Models

Globally, wind energy has proven to be one of the most sustainable sources of energy. Wind energy assessment plays a critical role on determining installation of wind turbines worldwide. This work presents the technical evaluation of wind energy potential using three available wind turbine models for prospective onshore wind farm in the southern Thailand at Krabi and Songkhla sites. Ten-minute interval wind data over a period of 3 to 4 years obtained from Weather Observing Station is utilized to observe the diurnal and monthly wind speed, as well as frequency distribution. WAsP program is applied for energy yield calculations and wind resource maps. Our results reveal that Krabi and Songkhla has the highest mean wind speed of 4.39 m/s in December and 3.91 m/s in February, respectively. The prevailing wind direction in Krabi and Songkhla are north-east and south-east, respectively. WAsP analyses show that the total net AEP for Krabi is 7163.782 MWh, 7762 MWh and 12731 MWh using 275 kW, 300 kW and 500 kW wind turbine models, respectively. Similarly, the total net AEP for Songkhla is 7116.63 MWh, 7775.245 MWh and 12390 MWh using 275 kW, 300 kW and 500 kW wind turbine models, respectively. The total capacity factor for Songkhla and Krabi is 29.1% and 28.3%, respectively. Our results indicate that Enercon E-40/5.40 500 kW wind turbine model produces the highest total gross AEP and total net AEP for Krabi and Songkhla sites. Besides, the Vergnet GEV MP-C 275 kW turbine model shows slightly higher capacity factor in case of both sites. The findings of this study reflect that small to medium size wind turbines can be utilized to generate electricity at the sites.