scholarly journals Assessment of CMIP6 Model Performance for Wind Speed in China

2021 ◽  
Vol 3 ◽  
Author(s):  
Lijun Zhao ◽  
Shuanglong Jin ◽  
Xiaolin Liu ◽  
Bo Wang ◽  
Zongpeng Song ◽  
...  
Keyword(s):  
Wind Speed ◽  
Model Performance ◽  
Coupled Model ◽  
Northwest China ◽  
Reanalysis Data ◽  
Central China ◽  
Green Development ◽  
Region I ◽  
Mean Wind Speed ◽  
Region Ii

As the major renewable energy, wind can greatly reduce carbon emissions. Following the “carbon neutral” strategy, wind power could help to achieve the realization of energy transformation and green development. Based on ERA5 reanalysis data and the multi-ensemble historical and scenario simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a variety of statistical analyses are used to evaluate the performance of CMIP6 simulating the wind speed in China. The conclusions are as follows: spatial patterns of the nine CMIP6 models are similar with ERA5, but BCC-CSM2-MR and MRI-ESM2-0 highly overestimate the wind speed in northwest China. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM behave better than the other six CMIP6 models in four specific regions are chosen for detailed study. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM tend to simulate a larger wind speed than ERA5 except the yearly averaged wind speed in region II and region IV. CESM2-WACCM and NorESM2-MM simulate a large monthly mean wind speed, but the value is relatively close with ERA5 in the summer. HadGEM3-GC31-MM overestimates wind speed in region I and region II from April to October, but gets closer with ERA during winter. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM simulate an increasing trend in Tibetan Plateau and Xinjiang in the next 100 years, while NorESM2-MM projects rising wind speed in the eastern part of Inner Mongolia, and HadGEM3-GC31-MM simulates increasing wind speed in the northeast and central China. The future wind speed in three models is projected to decline in region I, and the value of HadGEM3-GC31-MM is much larger. In region II, wind speed simulated by three models is projected to decrease, but the wind speed from HadGEM3-GC31-MM in region III and modeled wind speed in region IV from NorESM2-MM would climb with the slope equal to 0.0001 and 0.0012, respectively. This study indicates that the CMIP6 models have certain limitations to perform realistic wind changes, but CMIP6 could provide available reference for the projection of wind in specific areas.

scholarly journals The Potential Wind Power Resource in Central-South of the Constanta County

2019 ◽  
Vol 15 (3) ◽  
pp. 1-12
Author(s):  
Emilian Boboc
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Extreme Values ◽  
Reanalysis Data ◽  
Value Theory ◽  
Physical Models ◽  
Structural Safety ◽  
Energy Yield ◽  
Wind Speeds ◽  
Mean Wind Speed

Abstract Usually, wind turbine generator’s structures or radio masts are located in wind exposed sites. The paper aims to investigate the wind conditions in the nearby area of Cobadin Commune, Constanta County, Romania at heights of 150-200m above the surface using global reanalysis data sets CFSR, ERA 5, ERA I and MERRA 2. Using the extreme value theory and the physical models of the datasets, the research focuses on the assessment of the maximum values that are expected for the wind speeds, but the wind statistics created can be used for a further wind or energy yield calculation. Without reaching the survival wind speed for wind turbine generators, with mean wind speed values higher than 7 m/s and considering the cut-in and cut-out wind speeds of 3 m/s, respectively 25 m/s, the site can be exploited in more than 90% of the time to generate electricity, thus, the paper is addressed to the investors in the energy of renewable sources. At the same time, the insights of the wind characteristics and the knowledge of the extreme values of the wind speed can be useful, not just for the designers, in the rational assessment of the structural safety of wind turbines, but also those evaluating the insured losses.

scholarly journals Analysis of the Arctic polar vortex dynamics during the sudden stratospheric warming in January 2009

2021 ◽  
Vol 67 (2) ◽  
pp. 134-146
Author(s):  
V. V. Zuev ◽  
E. S. Savelieva ◽  
A. V. Pavlinsky
Keyword(s):  
Wind Speed ◽  
Polar Vortex ◽  
Reanalysis Data ◽  
Wave Activity ◽  
The Arctic ◽  
Stratospheric Warming ◽  
Sudden Stratospheric Warming ◽  
Mean Wind Speed ◽  
The Mean ◽  
Middle Stratosphere

The Arctic polar vortex is often affected by wave activity during its life cycle. The planetary Rossby waves propagating from the troposphere to the stratosphere occasionally lead to the displacement or splitting of the polar vortex, accompanied by sudden stratospheric warming (SSW). In January 2009, one of the largest SSWs was observed in the Arctic. In this work, the dynamics of the polar vortex during the 2009 SSW is considered using a new method that allows one to estimate the vortex area, the wind speed at the vortex edge, the mean temperature and ozone mass mixing ratio inside the vortex, based on the fact that the Arctic vortex edge at the 50 and 10 hPa pressure levels is determined by the geopotential values, respectively, 19.5. 104 and 29.5. 104 m2 /s2 , using the ERA5 reanalysis data. The application of this method is justified for the Arctic polar vortex, which is characterized by significant variability, especially during the period of its splitting. The splitting of the polar vortex in 2009 was observed on January 24 and 28, respectively, in the middle and lower stratosphere. About a week after the splitting, the vortices became closer in characteristics to small cyclones, which completely collapsed within 1–3 weeks. The influence of planetary wave activity on the polar vortex does not always lead to its breakdown. Short-term splitting of the polar vortex is sometimes observed for several days after which the polar vortex strengthens again and PSCs form inside the vortex. Such a recovery of the polar vortex is most likely to occur in the winter. Based on the analysis of the dynamics of the Arctic polar vortex for 1979–2020 and using the example of the 2009 SSW, we showed that when the vortex area decreases to less than 10 million km2 and the mean wind speed at the vortex edge decreases below 30 and 45 m/s, respectively, in the lower and middle stratosphere, the polar vortex becomes a small cyclone (with significantly higher temperatures within it), which usually collapses within 3 weeks.

scholarly journals Modified Approach to Reduce GCM Bias in Downscaled Precipitation: A Study in Ganga River Basin

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2097 ◽  
Author(s):  
Sharma ◽  
Ojha ◽  
Shukla ◽  
Pham ◽  
Linh ◽  
...  
Keyword(s):  
River Basin ◽  
General Circulation ◽  
Model Performance ◽  
Coupled Model ◽  
General Circulation Models ◽  
Skill Score ◽  
Reanalysis Data ◽  
Ganga River ◽  
Large Variability ◽  
Forest Regions

Reanalysis data is widely used to develop predictor-predictand models, which are further used to downscale coarse gridded general circulation models (GCM) data at a local scale. However, large variability in the downscaled product using different GCMs is still a big challenge. The first objective of this study was to assess the performance of reanalysis data to downscale precipitation using different GCMs. High bias in downscaled precipitation was observed using different GCMs, so a different downscaling approach is proposed in which historical data of GCM was used to develop a predictor-predictand model. The earlier approach is termed “Re-Obs” and the proposed approach as “GCM-Obs”. Both models were assessed using mathematical derivation and generated synthetic series. The intermodal bias in different GCMs downscaled precipitation using Re-Obs and GCM-Obs model was also checked. Coupled Model Inter-comparison Project-5 (CMIP5) data of ten different GCMs was used to downscale precipitation in different urbanized, rural, and forest regions in the Ganga river basin. Different measures were used to represent the relative performances of one downscaling approach over other approach in terms of closeness of downscaled precipitation with observed precipitation and reduction of bias using different GCMs. The effect of GCM spatial resolution in downscaling was also checked. The model performance, convergence, and skill score were computed to assess the ability of GCM-Obs and Re-Obs models. The proposed GCM-Obs model was found better than Re-Obs model to statistically downscale GCM. It was observed that GCM-Obs model was able to reduce GCM-Observed and GCM-GCM bias in the downscaled precipitation in the Ganga river basin.

scholarly journals Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data

2021 ◽  
Vol 6 (6) ◽  
pp. 1473-1490
Author(s):  
Alexander Basse ◽  
Doron Callies ◽  
Anselm Grötzner ◽  
Lukas Pauscher
Keyword(s):  
Wind Speed ◽  
Reference Data ◽  
Measurement Data ◽  
Theoretical Framework ◽  
Reanalysis Data ◽  
Seasonal Effects ◽  
Data Sets ◽  
Mean Wind Speed ◽  
Wind Measurements

Abstract. Measure–correlate–predict (MCP) approaches are often used to correct wind measurements to the long-term wind conditions on-site. This paper investigates systematic errors in MCP-based long-term corrections which occur if the measurement on-site covers only a few months (seasonal biases). In this context, two common linear MCP methods are tested and compared with regard to accuracy in mean, variance, and turbine energy production – namely, variance ratio (VR) and linear regression with residuals (LR). Wind measurement data from 18 sites with different terrain complexity in Germany are used (measurement heights between 100 and 140 m). Six different reanalysis data sets serve as the reference (long-term) wind data in the MCP calculations. All these reanalysis data sets showed an overpronounced annual course of wind speed (i.e., wind speeds too high in winter and too low in summer). However, despite the mathematical similarity of the two MCP methods, these errors in the data resulted in very different seasonal biases when either the VR or LR methods were used for the MCP calculations. In general, the VR method produced overestimations of the mean wind speed when measuring in summer and underestimations in the case of winter measurements. The LR method, in contrast, predominantly led to opposite results. An analysis of the bias in variance did not show such a clear seasonal variation. Overall, the variance error plays only a minor role for the accuracy in energy compared to the error in mean wind speed. Besides the experimental analysis, a theoretical framework is presented which explains these phenomena. This framework enables us to trace the seasonal biases to the mechanics of the methods and the properties of the reanalysis data sets. In summary, three aspects are identified as the main influential factors for the seasonal biases in mean wind speed: (1) the (dis-)similarity of the real wind conditions on-site in correlation and correction period (representativeness of the measurement period), (2) the capability of the reference data to reproduce the seasonal course of wind speed, and (3) the regression parameter β1 (slope) of the linear MCP method. This theoretical framework can also be considered valid for different measurement durations, other reference data sets, and other regions of the world.

scholarly journals Satellite-Derived Variation in Burned Area in China from 2001 to 2018 and Its Response to Climatic Factors

2021 ◽  
Vol 13 (7) ◽  
pp. 1287
Author(s):  
Xiaoxiao Wang ◽  
Zhenhua Di ◽  
Mei Li ◽  
Yunjun Yao
Keyword(s):  
Wind Speed ◽  
Climatic Factors ◽  
Pearson Correlation ◽  
Northwest China ◽  
Central China ◽  
Tibet Plateau ◽  
Burned Area ◽  
Driving Factor ◽  
Property Losses ◽  
Autumn And Winter

Fire is one of the most widespread and destructive disasters, which causes property losses, casualties, and disruption of the balance of ecosystems. Therefore, it is highly necessary for firefighting to study the variations in fire and its climatic attributions. This study analyzed the characteristics of fire-burned area (BA) and its response to climatic factors in seven subregions of China from 2001 to 2018 using satellite remote sensing BA products. The results show that the BA in China and most of its subregions shows a decreasing trend. In general, it is negatively correlated with precipitation and positively correlated with air temperature and wind speed based on the regression and correlation analyses. Based on Pearson correlation and random forest methods, it is also found that the temperature is commonly an important factor contributing to BA in China, except for R2 (Inner Mongolia region), where wind speed is more important, and R5 (South China), where precipitation is more important, which coexists at annual and seasonal scales. Besides temperature, precipitation in spring and summer is the main driving factor, such as in R1 (Northeast China), R5, R6 (Northwest China) and R7 (Qinghai–Tibet Plateau) in spring and R4 (Central China), R5 and R7 in summer; and wind speed in autumn and winter is the main driving factor, such as in R2 and R4 in autumn and R2, R3, R5, R6 and R7 in winter. Finally, the distributions of BA with respect to each climatic factor were also analyzed to quantify the range of climatic factors with maximum BA occurrence.

Prediction of Atmospheric Turbulence Characteristics for Surface Boundary Layer using Empirical Spectral Methods

2020 ◽  
Author(s):  
Yagya Dutta Dwivedi ◽  
Vasishta Bhargava Nukala ◽  
Satya Prasad Maddula ◽  
Kiran Nair
Keyword(s):  
Boundary Layer ◽  
Time Series ◽  
Surface Roughness ◽  
Wind Speed ◽  
Atmospheric Turbulence ◽  
Surface Boundary ◽  
Low Frequencies ◽  
Surface Boundary Layer ◽  
Power Spectral ◽  
Mean Wind Speed

Abstract Atmospheric turbulence is an unsteady phenomenon found in nature and plays significance role in predicting natural events and life prediction of structures. In this work, turbulence in surface boundary layer has been studied through empirical methods. Computer simulation of Von Karman, Kaimal methods were evaluated for different surface roughness and for low (1%), medium (10%) and high (50%) turbulence intensities. Instantaneous values of one minute time series for longitudinal turbulent wind at mean wind speed of 12 m/s using both spectra showed strong correlation in validation trends. Influence of integral length scales on turbulence kinetic energy production at different heights is illustrated. Time series for mean wind speed of 12 m/s with surface roughness value of 0.05 m have shown that variance for longitudinal, lateral and vertical velocity components were different and found to be anisotropic. Wind speed power spectral density from Davenport and Simiu profiles have also been calculated at surface roughness of 0.05 m and compared with k−1 and k−3 slopes for Kolmogorov k−5/3 law in inertial sub-range and k−7 in viscous dissipation range. At high frequencies, logarithmic slope of Kolmogorov −5/3rd law agreed well with Davenport, Harris, Simiu and Solari spectra than at low frequencies.

scholarly journals The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters

2018 ◽  
Vol 7 (2) ◽  
pp. 139-150 ◽  
Author(s):  
Adekunlé Akim Salami ◽  
Ayité Sénah Akoda Ajavon ◽  
Mawugno Koffi Kodjo ◽  
Seydou Ouedraogo ◽  
Koffi-Sa Bédja
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Time Series Data ◽  
Empirical Method ◽  
Likelihood Method ◽  
Series Data ◽  
New Approach ◽  
Weibull Parameters ◽  
Distribution Histogram ◽  
Mean Wind Speed

In this article, we introduced a new approach based on graphical method (GPM), maximum likelihood method (MLM), energy pattern factor method (EPFM), empirical method of Justus (EMJ), empirical method of Lysen (EML) and moment method (MOM) using the even or odd classes of wind speed series distribution histogram with 1 m/s as bin size to estimate the Weibull parameters. This new approach is compared on the basis of the resulting mean wind speed and its standard deviation using seven reliable statistical indicators (RPE, RMSE, MAPE, MABE, R2, RRMSE and IA). The results indicate that this new approach is adequate to estimate Weibull parameters and can outperform GPM, MLM, EPF, EMJ, EML and MOM which uses all wind speed time series data collected for one period. The study has also found a linear relationship between the Weibull parameters K and C estimated by MLM, EPFM, EMJ, EML and MOM using odd or even class wind speed time series and those obtained by applying these methods to all class (both even and odd bins) wind speed time series. Another interesting feature of this approach is the data size reduction which eventually leads to a reduced processing time.Article History: Received February 16th 2018; Received in revised form May 5th 2018; Accepted May 27th 2018; Available onlineHow to Cite This Article: Salami, A.A., Ajavon, A.S.A., Kodjo, M.K. , Ouedraogo, S. and Bédja, K. (2018) The Use of Odd and Even Class Wind Speed Time Series of Distribution Histogram to Estimate Weibull Parameters. Int. Journal of Renewable Energy Development 7(2), 139-150.https://doi.org/10.14710/ijred.7.2.139-150

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

scholarly journals Climate Transition from Warm-Dry to Warm-Wet in Eastern Northwest China

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 548
Author(s):  
Jinhu Yang ◽  
Qiang Zhang ◽  
Guoyang Lu ◽  
Xiaoyun Liu ◽  
Youheng Wang ◽  
...  
Keyword(s):  
Temperature Change ◽  
Water Resource Management ◽  
Yellow River ◽  
Asian Summer Monsoon ◽  
Coupled Model ◽  
Research Programme ◽  
Northwest China ◽  
Humid Climate ◽  
Synergistic Enhancement ◽  
Climate Transition

During the second half of the 20th century, eastern Northwest China experienced a warming and drying climate change. To determine whether this trend has continued or changed during the present century, this study systematically analyzes the characteristics of warming and dry–wet changes in eastern Northwest China based on the latest observational data and World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6) collection data. The results show that eastern Northwest China has warmed continuously during the past 60 years with a sudden temperature change occurring in the late 1990s. However, the temperature in the 2000s decreased slowly, and that in the 2010s showed a warming trend. The amount of precipitation began to increase in the late 1990s, which indicates a contemporary climate transition from warm-dry to warm-wet in eastern Northwest China. The contribution of precipitation to humidity is significantly more than that of temperature. Long-term and interannual variations dominate the temperature change, with the contribution of the former much stronger than that of the latter. However, interannual variation dominates the precipitation change. The warming accelerates from period to period, and the temperature spatial consistently increased during the three most recent climatic periods. The precipitation decreased from 1961–1990 to 1981–2010, whereas its spatial consistency increased from 1981–2010 to 1991–2019. The significant warming and humidification which began in the late 1990s and is expected to continue until the end of the 21st century in the medium emission scenario. However, the current sub-humid climate will not easily be changed. The warming could cause a climate transition from warm temperate to subtropical by 2040. The dry-to-wet climate transition in eastern Northwest China could be related to a synergistic enhancement of the East Asian summer monsoon and the westerly circulation. This research provides a scientific decision-making basis for implementing western development strategies, ecological protection, and high-quality development of the Yellow River Basin Area as well as that for ecological construction planning and water resource management of eastern Northwest China.

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