Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware los...Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware loss function is proposed for accurate multistep wind speed forecasting.In this model,the wind speed data is first denoised using the maximal overlap discrete wavelet transform.Next,an encoder-decoder network based on a temporal convolutional network,bidirectional gated recurrent unit,and multihead self-attention is employed for forecasting.Additionally,to enhance the ability of the model to identify temporal dynamics,a shape-aware loss function,ITILDE-Q,is employed in the model.To verify the effectiveness of the proposed model,a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds.Three error metrics and a similarity metric were adopted for comprehensive evaluation.The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios,with particularly pronounced differences in performance over longer forecast horizons.Furthermore,the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.展开更多
Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning...Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.展开更多
Previous studies have indicated a global reversal of near-surface wind speeds from a declining trend to an increasing trend around 2010;however,it remains unclear whether upper-air wind speeds exhibit a similar revers...Previous studies have indicated a global reversal of near-surface wind speeds from a declining trend to an increasing trend around 2010;however,it remains unclear whether upper-air wind speeds exhibit a similar reversal.This study evaluates reanalysis products using surface and radiosonde observations to analyze upper-air wind speed variations in the Northern Hemisphere,focusing on their seasonal and latitudinal differences.Results demonstrate that JRA-55 effectively captures wind speed variations in the Northern Hemisphere.Notably,upper-air wind speeds over land experienced a reversal in winter 2010 with significant latitudinal differences.The trend reversal of upper wind speed between the midlatitudes and subtropics presents a dipole pattern.From 1990 to 2010,upper-air wind speeds in the midlatitudes(40°-70°N)significantly declined,while the subtropical zone(20°-40°N)displayed an opposite trend.However,during 2010-2020,wind speeds in the midlatitudes shifted to a significant positive trend,whereas the subtropics experienced a significant negative trend.The variations in Northern Hemisphere winter wind speeds can be attributed to changes in low-level baroclinicity driven by tropical diabatic heating and midlatitude transient eddy feedback.Enhanced diabatic heating and weakened eddy feedback during 1990-2010 contributed to reduced wind speeds in the midlatitudes and increased speeds in the subtropics,while reduced diabatic heating and strengthened eddy feedback during 2010-2020 resulted in increased wind speeds in the midlatitudes and decreased speeds in the subtropics.The reversal of upper-air wind speeds could affect surface wind speeds by downward momentum transfer,which could contribute to the reversal of surface wind speeds.展开更多
A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical dis...A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.展开更多
Sea-surface wind is a vital meteorological element in marine activities and climate research.This study proposed the spectral attention enhanced multidimensional feature fusion convolutional long short-term memory(LST...Sea-surface wind is a vital meteorological element in marine activities and climate research.This study proposed the spectral attention enhanced multidimensional feature fusion convolutional long short-term memory(LSTM)network(SAMFF-Conv-LSTM),a novel approach for sea-surface wind-speed prediction that emphasizes the temporal characteristics of data samples.The model incorporates the Fourier transform to extract time-and frequency-domain features from wave and wind variables.For the 12 h prediction,the SAMFF-ConvLSTM achieved a correlation coefficient of 0.960 and a root mean square error(RMSE)of 1.350 m/s,implying a high prediction accuracy.For the 24 h prediction,the RMSE of the SAMFF-ConvLSTM was reduced by 38.11%,14.26%,and 13.36%compared with those of the convolutional neural network,gated recurrent units,and convolutional LSTM(ConvLSTM),respectively.These results confirm the superior reliability and accuracy of the SAMFF-ConvLSTM over traditional models in theoretical and practical applications.展开更多
Surface wind speed(SWS)not only plays a crucial role in regulating the Earth's energy and hydrological cycle,but also is an important source of sustainable renewable energy.This study assesses the credibility of s...Surface wind speed(SWS)not only plays a crucial role in regulating the Earth's energy and hydrological cycle,but also is an important source of sustainable renewable energy.This study assesses the credibility of sws in three reanalyses(ERA5,MERRA2,and JRA-55)in East Asia using both satellite and in-situ observations.Results show all three reanalyses can capture the spatial pattern of swS as in observations,yet there are notable differences in magnitude.On land,ERA5 and MERRA2 overestimate the SWS by about 0.6 and 1.5 m s^(-1),respectively,whereas JRA-55 underestimates it.The biases over the oceans are opposite to those on land and are relatively small due to the assimilation of observations of oceanic surface winds.Overall,JRA-55 and ERA5 offer better estimates of seasonal means and variances of SWS than MERRA2.The observed SWS shows a negative trend of-0.08 m s^(-1)/10 yr on land and a positive trend of 0.09 m s^(-1)/10 yr in the western North Pacific.Only JRA-55 shows similar trends to observations over both land and ocean,while ERA5 and MERRA2 show varying degrees of deviation from the observations.Further investigation shows that there is a strong link between the trend of SWS and that of the large-scale circulation,and that a large part of the SwS trend can be attributed to changes in large-scale circulations.展开更多
As the proportion of newenergy increases,the traditional cumulant method(CM)produces significant errorswhen performing probabilistic load flow(PLF)calculations with large-scale wind power integrated.Considering the wi...As the proportion of newenergy increases,the traditional cumulant method(CM)produces significant errorswhen performing probabilistic load flow(PLF)calculations with large-scale wind power integrated.Considering the wind speed correlation,a multi-scenario PLF calculation method that combines random sampling and segmented discrete wind farm power was proposed.Firstly,based on constructing discrete scenes of wind farms,the Nataf transform is used to handle the correlation between wind speeds.Then,the random sampling method determines the output probability of discrete wind power scenarios when wind speed exhibits correlation.Finally,the PLF calculation results of each scenario areweighted and superimposed following the total probability formula to obtain the final power flow calculation result.Verified in the IEEE standard node system,the absolute percent error(APE)for the mean and standard deviation(SD)of the node voltages and branch active power are all within 1%,and the average root mean square(AMSR)values of the probability curves are all less than 1%.展开更多
Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement...Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement precision.To address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind speed.By integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed data.Validation using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed forecasting.The proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.展开更多
Prediction of wind speed at high plateau airports can not only provide certain theoretical basis for the safe and efficient operation of the airports,but also save cost and time for their flight scheduling.In this pap...Prediction of wind speed at high plateau airports can not only provide certain theoretical basis for the safe and efficient operation of the airports,but also save cost and time for their flight scheduling.In this paper,based on the data of average wind speed and related meteorological factors at the meteorological station of Lhasa Gonggar Airport from 1964 to 2019,a prediction model of wind speed was constructed based on the support vector regression(SVR)algorithm.After the analysis of correlations between various meteorological features,significant features were selected by the random forest algorithm,thereby further improving the prediction performance of the model.The results indicate that both visibility and temperature having high correlations with wind speed are key features determining the final accuracy of the prediction model.Meanwhile,compared with other machine learning algorithms,the SVR algorithm represents more highlighted prediction performance for small sample data.展开更多
One of the cornerstones for guaranteeing the stability of wind generation and electric power system operation is wind speed prediction.This research offers a method based on Particle Swarm Optimization(PSO)to optimize...One of the cornerstones for guaranteeing the stability of wind generation and electric power system operation is wind speed prediction.This research offers a method based on Particle Swarm Optimization(PSO)to optimize the Bidirectional Long Short⁃term Memory Network(BiLSTM)in order to improve the wind speed prediction accuracy,taking into account the highly stochastic and regular aspects of wind speed.Firstly,the wind speed time sequence is subjected to the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).The complexity of the wind speed pattern is reduced by decomposing it into components with different local feature information.The BiLSTM model,which incorporates the attention mechanism for prediction,is then fitted to the decomposed data,and its parameters are optimized using the particle swarm technique,reducing errors in predictive modeling.To get the final prediction,the components are finally superimposed.The empirical evidence shows that the CEEMDAN⁃PSO⁃BiLSTM⁃attention model decreases the RMSE(Root⁃Mean⁃Square⁃Error)by 15%-44%,the MAE by 18%-45%,the MAPE by 24%-52%,and the R2 by 1.4%-2.7%in comparison to the BiLSTM and other models.The validation of CEEMDAN⁃PSO⁃BiLSTM⁃attention model in short⁃term wind speed prediction is verified.展开更多
Due to global warming and diminishing ice cover in Arctic regions,the northern sea route(NSR)has attracted increasing attention in recent years.Extreme cold temperatures and high wind speeds in Arctic regions present ...Due to global warming and diminishing ice cover in Arctic regions,the northern sea route(NSR)has attracted increasing attention in recent years.Extreme cold temperatures and high wind speeds in Arctic regions present substantial risks to vessels operating along the NSR.Consequently,analyzing extreme temperature and wind speed values along the NSR is essential for ensuring maritime operational safety in the region.This study analyzes wind and temperature data spanning 40 years,from 1981 to 2020,at four representative sites along the NSR for extreme value analysis.The average conditional exceedance rate(ACER)method and the Gumbel method are employed to estimate extreme wind speed and air temperature at these sites.Comparative analysis reveals that the ACER method provides higher accuracy and lower uncertainty in estimations.The predicted extreme wind speed for a 100-year return period is 30.36 m/s,with a minimum temperature of-56.66°C,varying across the four sites.Furthermore,the study presents extreme values corresponding to each return period,providing temperature extremes as a basis for guiding steel thickness specifications.These findings provide valuable reference for designing polar vessels and offshore structures,contributing to enhanced engineering standards for Arctic conditions.展开更多
The successful launch of the Cyclone Global Navigation Satellite System(CYGNSS)has opened an unprecedented opportunity for rapid observation of Wind Speed(WS)across vast oceanic regions.However,considerable debate per...The successful launch of the Cyclone Global Navigation Satellite System(CYGNSS)has opened an unprecedented opportunity for rapid observation of Wind Speed(WS)across vast oceanic regions.However,considerable debate persists over the choice of input feature parameters for WS retrieval models based on CYGNSS data,and enhancing the accuracy of WS retrieval is a focal point of current research.To address the aforementioned problems,this study establishes a comprehensive CYGNSS wind speed retrieval feature parameter set through an in-depth analysis of CYGNSS data,thereby providing a reference and basis for selecting input features for WS retrieval models.Through this analysis,we identified three crucial observational features:the normalized bistatic radar cross section,leading edge slope,and signal-to-noise ratio.Using these features,we developed a WS retrieval model based on the geophysical model function for CYGNSS data.Furthermore,acknowledging the intrinsic interconnection between wind and wave dynamics,we incorporate significant wave height into the WS retrieval model to further improve the WS retrieval accuracy.Comparative assessments with datasets from the European Centre for Medium-Range Weather Forecasts,the Chinese-French Oceanography Satellite Scatterometer,and buoy WS data underscore the high accuracy of our model,demonstrating its utility as a valuable tool for research in ocean dynamics and marine environmental prediction.展开更多
This study investigates the vertical variations of aerosol size distribution(0.06-1??m)and cloud condensation nuclei(CCN)spectra over the Southern Ocean(SO)using aircraft observations from the SOCRATES campaign.Result...This study investigates the vertical variations of aerosol size distribution(0.06-1??m)and cloud condensation nuclei(CCN)spectra over the Southern Ocean(SO)using aircraft observations from the SOCRATES campaign.Results reveal a bimodal aerosol size distribution within the marine boundary layer(MBL),with peaks at diameters of~0.06??m and~0.65??m,dominated by sea-salt particles.Accumulation-mode aerosol concentrations decrease with altitude within the MBL,while Aitken-mode aerosol concentrations peak above the MBL(~2-3 km).Wind speed strongly correlates with coarse-mode aerosol concentration(R~2=0.77),implicating sea spray as a major CCN source at low supersaturations(SS=0.1%).The altitudes of CCN concentration peaks shift from the MBL(<1 km,SS<0.4%)to the free troposphere(~2.5 km,SS>0.4%),suggesting new particle formation aloft,distinct from sea surface sources.These findings highlight the unique aerosol-CCN dynamics in the pristine SO,offering critical constraints for models simulating cloud-aerosol interactions in preindustrial-like environments.展开更多
Based on the data of the wind speed from 20 m meteorological tower and PM10 mass concentration in Zhurihe region from January of 2005 to April of 2006,the evolution characteristics of wind speed profile in near surfac...Based on the data of the wind speed from 20 m meteorological tower and PM10 mass concentration in Zhurihe region from January of 2005 to April of 2006,the evolution characteristics of wind speed profile in near surface layer and PM10 in three representative dust weather processes (dust storm,blowing sand and floating dust) were analyzed.The results showed that wind speed was higher during dust storm and blowing sand with remarkable vertical gradient.The speed in floating dust was relatively lower and increased during the whole process.In general,wind speed after dust weather was smaller with respect to that before the event.The average mass concentrations of PM10 in the processes of dust storm,blowing sand and floating dust were in the ranges of 5 436.38-10 000,1 799.49-4 006.06 and 1 765.53 μg/m3,respectively.展开更多
This article deals with an experimental study on the aerodynamic characteristics of a low-drag high-speed nature laminar flow (NLF) airfoil for business airplanes in the TST27 wind tunnel at Delft University of Techno...This article deals with an experimental study on the aerodynamic characteristics of a low-drag high-speed nature laminar flow (NLF) airfoil for business airplanes in the TST27 wind tunnel at Delft University of Technology, the Netherlands. In this experiment, in an attempt to reduce the errors of measurement and improve its accuracy in high-speed flight, some nonintrusive meas- urement techniques, such as the quantitative infrared thermography (IRT), the digital particle imaging velocimetry (PIV), and the s...展开更多
In the study a fire and fire environment model is set up and by using PHEONICS software 3 cases of surface fires are studied. The results fit the experimental studies well generally. The simulation reveals that (1) Th...In the study a fire and fire environment model is set up and by using PHEONICS software 3 cases of surface fires are studied. The results fit the experimental studies well generally. The simulation reveals that (1) The wind speed fields in front of fire front generally can be divided into 3 zones and there is always an eddy immediately at the corner between just in front of the fire and the ground. (2) The shape and dimension of the division of the 3 zones is mainly decided by slope angle and ambient wind speed given fire line intensity. (3) There exits an upwind zone in front of fire front. Ambient wind speeds have little effect on the magnitude of the upwind speed when slope angle is 0. But when the slope angle is negative, the upwind is apparently stronger.展开更多
Based on the multi-loop method, the rotating torque and speed of theinduction machine are analyzed. The fluctuating components of the torque and speed caused by rotorwinding faults are studied. The models for calculat...Based on the multi-loop method, the rotating torque and speed of theinduction machine are analyzed. The fluctuating components of the torque and speed caused by rotorwinding faults are studied. The models for calculating the fluctuating components are put forward.Simulation and computation results show that the rotor winding faults will cause electromagnetictorque and rotating speed to fluctuate; and fluctuating frequencies are the same and their magnitudewill increase with the rise of the severity of the faults. The load inertia affects the torque andspeed fluctuation, with the increase of inertia, the fluctuation of the torque will rise, while thecorresponding speed fluctuation will obviously decline.展开更多
Comparing and analyzing the difference between automatic-observed and manual-observed wind speed based on the wind speed parallel observations in two methods, we find that many elements can influence the difference be...Comparing and analyzing the difference between automatic-observed and manual-observed wind speed based on the wind speed parallel observations in two methods, we find that many elements can influence the difference between automatic-observed and manual-observed wind speed, including the levels of speed wind, observation instruments and different regions. According to these elements, correction has been conducted, and find that the correction according to the level of wind speed has the best correction effect.展开更多
For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derive...For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derived successfully using wind speed data estimated by the TOPEX satellite altimeter. From the results we find that： （1） the mean sea surface roughness in winter is greater than in summer; （2） compared with other sea areas, the sea surface roughness in the sea area east of Japan （ N30°- 40°, E135°- 150°） is larger than in other sea areas; （3） sea surface roughness in the South China Sea changes more greatly than that in the Bohai Sea, Yellow Sea and East China Sea.展开更多
基金supported by the National Natural Science Foundation of China(No.52171284)。
文摘Wind speed is a crucial parameter affecting wind energy utilization.However,its volatility leads to time-varying power output.Herein,a novel Seq2Seq model integrating deep learning,data denoising,and a shape-aware loss function is proposed for accurate multistep wind speed forecasting.In this model,the wind speed data is first denoised using the maximal overlap discrete wavelet transform.Next,an encoder-decoder network based on a temporal convolutional network,bidirectional gated recurrent unit,and multihead self-attention is employed for forecasting.Additionally,to enhance the ability of the model to identify temporal dynamics,a shape-aware loss function,ITILDE-Q,is employed in the model.To verify the effectiveness of the proposed model,a comparative experiment and an ablation experiment were conducted using three datasets of measured wind speeds.Three error metrics and a similarity metric were adopted for comprehensive evaluation.The experimental results showed that the proposed model consistently outperforms benchmark models in all tested forecasting scenarios,with particularly pronounced differences in performance over longer forecast horizons.Furthermore,the synergistic interaction of the three key components contributes to the extraordinary performance of the proposed model.
基金funded by Science and Technology Research and Development Program Project of China Railway Group Limited(No.2023-Major-02)National Natural Science Foundation of China(Grant No.52378200)Sichuan Science and Technology Program(Grant No.2024NSFSC0017).
文摘Accurate wind speed prediction is crucial for stabilizing power grids with high wind energy penetration.This study presents a novel machine learning model that integrates clustering,deep learning,and transfer learning to mitigate accuracy degradation in 24-h forecasting.Initially,an optimized DB-SCAN(Density-Based Spatial Clustering of Applications with Noise)algorithm clusters wind fields based on wind direction,probability density,and spectral features,enhancing physical interpretability and reducing training complexity.Subsequently,a ResNet(Residual Network)extracts multi-scale patterns from decomposed wind signals,while transfer learning adapts the backbone network across clusters,cutting training time by over 90%.Finally,a CBAM(Convolutional Block Attention Module)attention mechanism is employed to prioritize features for LSTM-based prediction.Tested on the 2015 Jena wind speed dataset,the model demonstrates superior accuracy and robustness compared to state-of-the-art baselines.Key innovations include:(a)Physics-informed clustering for interpretable wind regime classification;(b)Transfer learning with deep feature extraction,preserving accuracy while minimizing training time;and(c)On the 2016 Jena wind speed dataset,the model achieves MAPE(Mean Absolute Percentage Error)values of 16.82%and 18.02%for the Weibull-shaped and Gaussian-shaped wind speed clusters,respectively,demonstrating the model’s robust generalization capacity.This framework offers an efficient and effective solution for long-term wind forecasting.
基金supported by the National Natural Science Foundation of China[grant numbers U2442207,42122034,42075043,and 42330609]the Youth Innovation Promotion Association[grant number 2021427]+2 种基金the West Light Foundation[grant number xbzgzdsys-202409]of the Chinese Academy of Sciencesthe Key Talent Projects in Gansu Provincethe Central Guidance Fund for Local Science and Technology Development Projects in Gansu Province[grant number 24ZYQA031].
文摘Previous studies have indicated a global reversal of near-surface wind speeds from a declining trend to an increasing trend around 2010;however,it remains unclear whether upper-air wind speeds exhibit a similar reversal.This study evaluates reanalysis products using surface and radiosonde observations to analyze upper-air wind speed variations in the Northern Hemisphere,focusing on their seasonal and latitudinal differences.Results demonstrate that JRA-55 effectively captures wind speed variations in the Northern Hemisphere.Notably,upper-air wind speeds over land experienced a reversal in winter 2010 with significant latitudinal differences.The trend reversal of upper wind speed between the midlatitudes and subtropics presents a dipole pattern.From 1990 to 2010,upper-air wind speeds in the midlatitudes(40°-70°N)significantly declined,while the subtropical zone(20°-40°N)displayed an opposite trend.However,during 2010-2020,wind speeds in the midlatitudes shifted to a significant positive trend,whereas the subtropics experienced a significant negative trend.The variations in Northern Hemisphere winter wind speeds can be attributed to changes in low-level baroclinicity driven by tropical diabatic heating and midlatitude transient eddy feedback.Enhanced diabatic heating and weakened eddy feedback during 1990-2010 contributed to reduced wind speeds in the midlatitudes and increased speeds in the subtropics,while reduced diabatic heating and strengthened eddy feedback during 2010-2020 resulted in increased wind speeds in the midlatitudes and decreased speeds in the subtropics.The reversal of upper-air wind speeds could affect surface wind speeds by downward momentum transfer,which could contribute to the reversal of surface wind speeds.
基金supported by the Key R&D Program of Shandong Province,China(No.2021ZLGX04)the National Natural Science Foundation of China(No.52171284)。
文摘A joint statistical model of wind speed and wind shear is critical for height-dependent wind resource characteristic analysis.However,given the different atmospheric conditions that may be involved,the statistical distribution of the two variables may show multimodal characteristics.In this work,a finite mixture bivariate statistical model was designed to describe the statistical properties,which is composed of several components,each with a Weibull distribution and a normal distribution for wind speed and wind shear,respectively,with a Gaussian copula to describe the dependency structure between the two variables.To confirm the developed model,reanalysis data from six positions in the coastal sea areas of China were used.Our results disclosed that the developed joint statistical model can accurately capture the different multimodal structures presented in all the bivariate samples under mixed atmospheric conditions,giving acceptable predictions of the joint probability distributions.Proper consideration of wind shear coefficient variation is crucial in estimating height-dependent wind resource characteristics.Importantly,unlike traditional methods that are limited to specific hub heights,the model developed here can estimate wind energy potential across different hub heights,enhancing the economic viability assessment of wind power projects.
基金supported by the National Natural Science Foundation(No.42176020)the Open Research Fund of State Key Laboratory of Target Vulnerability Assessment(No.YSX2024KFYS001)+1 种基金the National Key Research and Development Program(No.2022YFC3105002)the Project from Key Laboratory of Marine Environmental Information Technology(No.2023GFW-1047).
文摘Sea-surface wind is a vital meteorological element in marine activities and climate research.This study proposed the spectral attention enhanced multidimensional feature fusion convolutional long short-term memory(LSTM)network(SAMFF-Conv-LSTM),a novel approach for sea-surface wind-speed prediction that emphasizes the temporal characteristics of data samples.The model incorporates the Fourier transform to extract time-and frequency-domain features from wave and wind variables.For the 12 h prediction,the SAMFF-ConvLSTM achieved a correlation coefficient of 0.960 and a root mean square error(RMSE)of 1.350 m/s,implying a high prediction accuracy.For the 24 h prediction,the RMSE of the SAMFF-ConvLSTM was reduced by 38.11%,14.26%,and 13.36%compared with those of the convolutional neural network,gated recurrent units,and convolutional LSTM(ConvLSTM),respectively.These results confirm the superior reliability and accuracy of the SAMFF-ConvLSTM over traditional models in theoretical and practical applications.
基金supported by the National Natural Science Foundation of China[grant numbers 42361144708,42205041,and 42175165]a scientific research project of the Shanghai Investigation,Design and Research Institute Co.,Ltd.[grant number 2023CN(83)-001]the National Key Scientific and Technological Infrastructure project“Earth System Science Numerical Simulator Facility”(EarthLab).
文摘Surface wind speed(SWS)not only plays a crucial role in regulating the Earth's energy and hydrological cycle,but also is an important source of sustainable renewable energy.This study assesses the credibility of sws in three reanalyses(ERA5,MERRA2,and JRA-55)in East Asia using both satellite and in-situ observations.Results show all three reanalyses can capture the spatial pattern of swS as in observations,yet there are notable differences in magnitude.On land,ERA5 and MERRA2 overestimate the SWS by about 0.6 and 1.5 m s^(-1),respectively,whereas JRA-55 underestimates it.The biases over the oceans are opposite to those on land and are relatively small due to the assimilation of observations of oceanic surface winds.Overall,JRA-55 and ERA5 offer better estimates of seasonal means and variances of SWS than MERRA2.The observed SWS shows a negative trend of-0.08 m s^(-1)/10 yr on land and a positive trend of 0.09 m s^(-1)/10 yr in the western North Pacific.Only JRA-55 shows similar trends to observations over both land and ocean,while ERA5 and MERRA2 show varying degrees of deviation from the observations.Further investigation shows that there is a strong link between the trend of SWS and that of the large-scale circulation,and that a large part of the SwS trend can be attributed to changes in large-scale circulations.
基金supported by Basic Science Research Program through the National Natural Science Foundation of China(Grant No.61867003).
文摘As the proportion of newenergy increases,the traditional cumulant method(CM)produces significant errorswhen performing probabilistic load flow(PLF)calculations with large-scale wind power integrated.Considering the wind speed correlation,a multi-scenario PLF calculation method that combines random sampling and segmented discrete wind farm power was proposed.Firstly,based on constructing discrete scenes of wind farms,the Nataf transform is used to handle the correlation between wind speeds.Then,the random sampling method determines the output probability of discrete wind power scenarios when wind speed exhibits correlation.Finally,the PLF calculation results of each scenario areweighted and superimposed following the total probability formula to obtain the final power flow calculation result.Verified in the IEEE standard node system,the absolute percent error(APE)for the mean and standard deviation(SD)of the node voltages and branch active power are all within 1%,and the average root mean square(AMSR)values of the probability curves are all less than 1%.
基金supported by the Major Innovation Project for the Integration of Science,Education,and Industry of Qilu University of Technology(Shandong Academy of Sciences)(Nos.2023HYZX01,2023JBZ02)the Open Project of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)(No.2023ZD007)+2 种基金the Talent Research Projects of Qilu University of Technology(Shandong Academy of Sciences)(No.2023RCKY136)the Technology and Innovation Major Project of the Ministry of Science and Technology of China(No.2022ZD0118600)the Jinan‘20 New Colleges and Universities’Funded Project(No.202333043)。
文摘Accurate wind speed measurements on maritime vessels are crucial for weather forecasting,sea state prediction,and safe navigation.However,vessel motion and challenging environmental conditions often affect measurement precision.To address this issue,this study proposes an innovative framework for correcting and predicting shipborne wind speed.By integrating a main network with a momentum updating network,the proposed framework effectively extracts features from the time and frequency domains,thereby allowing for precise adjustments and predictions of shipborne wind speed data.Validation using real sensor data collected at the Qingdao Oceanographic Institute demonstrates that the proposed method outperforms existing approaches in single-and multi-step predictions compared to existing methods,achieving higher accuracy in wind speed forecasting.The proposed innovative approach offers a promising direction for future validation in more realistic maritime onboard scenarios.
文摘Prediction of wind speed at high plateau airports can not only provide certain theoretical basis for the safe and efficient operation of the airports,but also save cost and time for their flight scheduling.In this paper,based on the data of average wind speed and related meteorological factors at the meteorological station of Lhasa Gonggar Airport from 1964 to 2019,a prediction model of wind speed was constructed based on the support vector regression(SVR)algorithm.After the analysis of correlations between various meteorological features,significant features were selected by the random forest algorithm,thereby further improving the prediction performance of the model.The results indicate that both visibility and temperature having high correlations with wind speed are key features determining the final accuracy of the prediction model.Meanwhile,compared with other machine learning algorithms,the SVR algorithm represents more highlighted prediction performance for small sample data.
基金Sponsored by Science Research Project of Liaoning Education Department(Grant No.LJKZ0143)Open Project of State Key Laboratory of Syn⁃thetical Automation for Process Industries(Grant No.2023⁃kfkt⁃01).
文摘One of the cornerstones for guaranteeing the stability of wind generation and electric power system operation is wind speed prediction.This research offers a method based on Particle Swarm Optimization(PSO)to optimize the Bidirectional Long Short⁃term Memory Network(BiLSTM)in order to improve the wind speed prediction accuracy,taking into account the highly stochastic and regular aspects of wind speed.Firstly,the wind speed time sequence is subjected to the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN).The complexity of the wind speed pattern is reduced by decomposing it into components with different local feature information.The BiLSTM model,which incorporates the attention mechanism for prediction,is then fitted to the decomposed data,and its parameters are optimized using the particle swarm technique,reducing errors in predictive modeling.To get the final prediction,the components are finally superimposed.The empirical evidence shows that the CEEMDAN⁃PSO⁃BiLSTM⁃attention model decreases the RMSE(Root⁃Mean⁃Square⁃Error)by 15%-44%,the MAE by 18%-45%,the MAPE by 24%-52%,and the R2 by 1.4%-2.7%in comparison to the BiLSTM and other models.The validation of CEEMDAN⁃PSO⁃BiLSTM⁃attention model in short⁃term wind speed prediction is verified.
基金supported by the National Natural Science Foundation of China(Grant No.52201379)the Fundamental Research Funds for the Central Universities(Grant No.WUT:3120622898)+2 种基金State Key Laboratory of Structural Analysis,Optimization and CAE Software for Industrial Equipment,Dalian University of Technology(Grant No.GZ 231088)Shanghai Key Laboratory of Naval Architecture Engineering(Grant No.SE202305)funded by European Research Council project under the European Union’s Horizon 2020 research and innovation program(Grant No.TRUST CoG 2019864724).
文摘Due to global warming and diminishing ice cover in Arctic regions,the northern sea route(NSR)has attracted increasing attention in recent years.Extreme cold temperatures and high wind speeds in Arctic regions present substantial risks to vessels operating along the NSR.Consequently,analyzing extreme temperature and wind speed values along the NSR is essential for ensuring maritime operational safety in the region.This study analyzes wind and temperature data spanning 40 years,from 1981 to 2020,at four representative sites along the NSR for extreme value analysis.The average conditional exceedance rate(ACER)method and the Gumbel method are employed to estimate extreme wind speed and air temperature at these sites.Comparative analysis reveals that the ACER method provides higher accuracy and lower uncertainty in estimations.The predicted extreme wind speed for a 100-year return period is 30.36 m/s,with a minimum temperature of-56.66°C,varying across the four sites.Furthermore,the study presents extreme values corresponding to each return period,providing temperature extremes as a basis for guiding steel thickness specifications.These findings provide valuable reference for designing polar vessels and offshore structures,contributing to enhanced engineering standards for Arctic conditions.
基金The Fund of Key Laboratory of Space Ocean Remote Sensing and Application,Ministry of Natural Resources under contract No.2023CFO016the National Natural Science Foundation of China under contract No.61931025the Key Program of Joint Fund of the National Natural Science Foundation of China and Shandong Province under contract No.U22A20586.
文摘The successful launch of the Cyclone Global Navigation Satellite System(CYGNSS)has opened an unprecedented opportunity for rapid observation of Wind Speed(WS)across vast oceanic regions.However,considerable debate persists over the choice of input feature parameters for WS retrieval models based on CYGNSS data,and enhancing the accuracy of WS retrieval is a focal point of current research.To address the aforementioned problems,this study establishes a comprehensive CYGNSS wind speed retrieval feature parameter set through an in-depth analysis of CYGNSS data,thereby providing a reference and basis for selecting input features for WS retrieval models.Through this analysis,we identified three crucial observational features:the normalized bistatic radar cross section,leading edge slope,and signal-to-noise ratio.Using these features,we developed a WS retrieval model based on the geophysical model function for CYGNSS data.Furthermore,acknowledging the intrinsic interconnection between wind and wave dynamics,we incorporate significant wave height into the WS retrieval model to further improve the WS retrieval accuracy.Comparative assessments with datasets from the European Centre for Medium-Range Weather Forecasts,the Chinese-French Oceanography Satellite Scatterometer,and buoy WS data underscore the high accuracy of our model,demonstrating its utility as a valuable tool for research in ocean dynamics and marine environmental prediction.
基金supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.42430601,42175087)the Science and Technology Project of Gansu Province(Outstanding Youth Fund,Grant No.24JRRA386)the Fundamental Research Funds for the Central Universities(Grant No.lzujbky-2024-jdzx04)。
文摘This study investigates the vertical variations of aerosol size distribution(0.06-1??m)and cloud condensation nuclei(CCN)spectra over the Southern Ocean(SO)using aircraft observations from the SOCRATES campaign.Results reveal a bimodal aerosol size distribution within the marine boundary layer(MBL),with peaks at diameters of~0.06??m and~0.65??m,dominated by sea-salt particles.Accumulation-mode aerosol concentrations decrease with altitude within the MBL,while Aitken-mode aerosol concentrations peak above the MBL(~2-3 km).Wind speed strongly correlates with coarse-mode aerosol concentration(R~2=0.77),implicating sea spray as a major CCN source at low supersaturations(SS=0.1%).The altitudes of CCN concentration peaks shift from the MBL(<1 km,SS<0.4%)to the free troposphere(~2.5 km,SS>0.4%),suggesting new particle formation aloft,distinct from sea surface sources.These findings highlight the unique aerosol-CCN dynamics in the pristine SO,offering critical constraints for models simulating cloud-aerosol interactions in preindustrial-like environments.
基金Supported by the Scientific Project of Jiangsu Environmental Protection(2009008)The Preliminary Research Projects of Jiangsu "Shier Wu" Environmental Protection Planning
文摘Based on the data of the wind speed from 20 m meteorological tower and PM10 mass concentration in Zhurihe region from January of 2005 to April of 2006,the evolution characteristics of wind speed profile in near surface layer and PM10 in three representative dust weather processes (dust storm,blowing sand and floating dust) were analyzed.The results showed that wind speed was higher during dust storm and blowing sand with remarkable vertical gradient.The speed in floating dust was relatively lower and increased during the whole process.In general,wind speed after dust weather was smaller with respect to that before the event.The average mass concentrations of PM10 in the processes of dust storm,blowing sand and floating dust were in the ranges of 5 436.38-10 000,1 799.49-4 006.06 and 1 765.53 μg/m3,respectively.
文摘This article deals with an experimental study on the aerodynamic characteristics of a low-drag high-speed nature laminar flow (NLF) airfoil for business airplanes in the TST27 wind tunnel at Delft University of Technology, the Netherlands. In this experiment, in an attempt to reduce the errors of measurement and improve its accuracy in high-speed flight, some nonintrusive meas- urement techniques, such as the quantitative infrared thermography (IRT), the digital particle imaging velocimetry (PIV), and the s...
基金TheresearchissupportedbyFoundationforDoctoralStudiesofMinistryofEducation (No .19980 0 2 2 0 6 )
文摘In the study a fire and fire environment model is set up and by using PHEONICS software 3 cases of surface fires are studied. The results fit the experimental studies well generally. The simulation reveals that (1) The wind speed fields in front of fire front generally can be divided into 3 zones and there is always an eddy immediately at the corner between just in front of the fire and the ground. (2) The shape and dimension of the division of the 3 zones is mainly decided by slope angle and ambient wind speed given fire line intensity. (3) There exits an upwind zone in front of fire front. Ambient wind speeds have little effect on the magnitude of the upwind speed when slope angle is 0. But when the slope angle is negative, the upwind is apparently stronger.
文摘Based on the multi-loop method, the rotating torque and speed of theinduction machine are analyzed. The fluctuating components of the torque and speed caused by rotorwinding faults are studied. The models for calculating the fluctuating components are put forward.Simulation and computation results show that the rotor winding faults will cause electromagnetictorque and rotating speed to fluctuate; and fluctuating frequencies are the same and their magnitudewill increase with the rise of the severity of the faults. The load inertia affects the torque andspeed fluctuation, with the increase of inertia, the fluctuation of the torque will rise, while thecorresponding speed fluctuation will obviously decline.
基金Supported by Meteorological Data Sharing Center Project (2005DKA31700-01,GX07-01-01)2009 Specific Research in Non-profit Sector (200906041-053)
文摘Comparing and analyzing the difference between automatic-observed and manual-observed wind speed based on the wind speed parallel observations in two methods, we find that many elements can influence the difference between automatic-observed and manual-observed wind speed, including the levels of speed wind, observation instruments and different regions. According to these elements, correction has been conducted, and find that the correction according to the level of wind speed has the best correction effect.
文摘For open sea conditions the sea surface roughness is described as a function of surface stress and wind speed over sea surface by Charnock relation. The sea surface roughnessn in the North-west Pacific Ocean is derived successfully using wind speed data estimated by the TOPEX satellite altimeter. From the results we find that： （1） the mean sea surface roughness in winter is greater than in summer; （2） compared with other sea areas, the sea surface roughness in the sea area east of Japan （ N30°- 40°, E135°- 150°） is larger than in other sea areas; （3） sea surface roughness in the South China Sea changes more greatly than that in the Bohai Sea, Yellow Sea and East China Sea.
