Temporal Variability Of Wind Speed Research Articles

Temporal variation of wind speed and wind direction and interrelationship between air temperature and wind speed for different climatic seasons Northern State, Sudan

The research aims to study the temporal variability of wind speed and direction and their correlation with air temperature in different climatic seasons in Northern State, to produce broad data base on winds variability and interrelationship between air temperature and wind speed for different climatic seasons. Mean monthly and daily climatic data dating back to 2001(first season) and 2011(second season) were obtained from Karima meteorological station representing three different climatic seasons namely: April for summer, August for autumn and December for winter. Data were subjected to simple regression analysis, statistical analysis and computations were done by “Excel 2000”. The results showed that the wind speed varied widely during each month and day even within the same climatic season. In two seasons, the diurnal variation for different climatic seasons Apr., Aug. and Dec. was much lower than the monthly; due to the higher monthly variability of wind speed. The results also revealed that the prevailing direction of winds was generally northwards. The results of April in the first season are presented as an example; the mean daily wind speed (Va) ranged from 0.6 to 17m/s, with a mean of 7.2m/s, a STD of 4.3 and a CV of 60%. The daily mean data ranged from 5.4 (3hrs) to 8.2 (24hrs), with a mean of 7.2m/s, a STD of 1.1 and a CV of 15.3%.The prevailing wind direction decreases in the following order: N>NE=NNE>NNW. The monthly variability of the air temperature in the two seasons decreased in the following order: winter> summer> autumn. Significant, inversely proportional and cubic wind speed-temperature correlations in the two seasons for Aug. and Dec. were (P<0.05, r=0.362); (P<0.01, r= 0.488); (P<0.001, R=585) and (P<0.001, r= 0.679), respectively.

Space, Time, and Size Dependencies of Greenhouse Gas Payback Times of Wind Turbines in Northwestern Europe.

The net greenhouse gas benefits of wind turbines compared to their fossil energy counterparts depend on location-specific wind climatology and the turbines’ technological characteristics. Assessing the environmental impact of individual wind parks requires a universal but location-dependent method. Here, the greenhouse gas payback time for 4161 wind turbine locations in northwestern Europe was determined as a function of (i) turbine size and (ii) spatial and temporal variability in wind speed. A high-resolution wind atlas (hourly wind speed data between 1979 and 2013 on a 2.5 by 2.5 km grid) was combined with a regression model predicting the wind turbines’ life cycle greenhouse gas emissions from turbine size. The greenhouse gas payback time of wind turbines in northwestern Europe varied between 1.8 and 22.5 months, averaging 5.3 months. The spatiotemporal variability in wind climatology has a particularly large influence on the payback time, while the variability in turbine size is of lesser importance. Applying lower-resolution wind speed data (daily on a 30 by 30 km grid) approximated the high-resolution results. These findings imply that forecasting location-specific greenhouse gas payback times of wind turbines globally is well within reach with the availability of a high-resolution wind climatology in combination with technological information.

Application of weather forecasting model WRF for operational electric power network management—a case study for Phailin cyclone

Extreme weather events like tropical cyclone result in colossal catastrophe during landfall causing widespread inland flooding due to storm surge and also the post-landfall event result in extensive damage to infrastructural facilities and property hinterland. The state of Odisha located in east coast of India experienced a Very Severe Cyclonic Storm (VSCS) named Phailin during the post-monsoon season of October 2013. Timely warnings and alertness on storm surge coordinated with a massive evacuation effort by National Disaster Management Authorities (NDMA) were quite effective in minimizing the loss of human life. However, there was a trial of destruction due to extremely high winds and rainfall that followed during post-landfall causing extensive damage to property and major infrastructure facilities in the Odisha State. This study critically investigated the Phailin post-landfall phase focusing on the impact of high winds and rainfall on the power distribution network using the Weather Research and Forecasting (WRF) model. The study evaluated the spatial and temporal variability of wind speed and rainfall distribution from the WRF model configured for three different spatial domains and selecting the best available microphysics and land surface parameterization schemes. The proposed outer, intermediate, and inner domains had spatial resolutions of 27, 9, and 3 km respectively and that provided the best estimate for onshore wind speed, track forecast, and rainfall distribution highly relevant for the management of power distribution and transmission network. In context to weather model application for the Indian region, this effort is novel and probably for the first time that linked a suitable customized weather model output to evaluate its impact on observed tripping in transmission network of electric power grids. The dynamic model outputs from WRF were compared with data from synchrophasors used in electrical technology that monitored the transient and dynamic behavior of power systems in real-time operations. A close examination of the results signifies that the atmospheric model performed exceptionally well in capturing the tripping time of power lines, and the overall knowledge obtained from this study has a broader scope to develop a framework for efficient planning operations of the power network, resource allocation, and emergency preparedness.

Climate driven variability of wind-waves in the Red Sea

Wind-wave variability in the Red Sea has been studied over a period of 32 years (1979–2010) by hindcasting the wind-waves using a third generation spectral wave model, WaveWatch III, forced with the Climate Forecast System Reanalysis (CFSR) winds. The wave model results have been validated against the satellite observations and available measurements in the Red Sea. The study reveals robust spatial and temporal variability of wind speed and significant wave height in the Red Sea. The role of climatic indices on the wind-wave variability has been assessed. The highest extreme wave heights (99th percentile) are centered at three regions; around 23° N, 19° N, and 15° N latitudes, while the 90th percentile and mean significant wave heights are scattered in the deep waters of the Red Sea. Contrasting trends of the significant wave heights such as negative trends in the northern Red Sea during summer and positive trends in the southern Red Sea during winter are the highlights, where the trends are significant and comparable with the trends at few marginal seas around the globe. A clear distinction exists between the two parts of the Red Sea in response of the variability due to climatic indices. Among the climatic indices, the North Atlantic Oscillation (NAO) and the El Niño-Southern Oscillation (ENSO) show significant positive and negative correlations with the characteristic wave heights in the northern and southern Red Sea.

Intra-farm wind speed variability observed by nacelle anemometers in a large inland wind farm

Using 11-month, 15-min averaged measurements by nacelle anemometers at 274 wind turbines, we studied the spatial and temporal variability of wind speed in a wind farm over an area of about 20km by 20km located in mountainous terrain. The intra-farm variability is compared between winter and summer: during summer the dominant time scale of wind speed fluctuation is the diurnal cycle of 24h. During winter, however, the dominant scale is 48h. The spatial variability of wind speed on scales ≤O(10km) is closely associated with the temporal variability on scales ≤O(10h). Over a time span ≤3h rapid drop-offs in wind speed occur more often than rapid increases, and the rapid drop-offs are more frequent during summer than winter. The intra-farm spatial variability of wind speed has a diurnal cycle with that reaches its maximum in the nighttime and its minimum around midday. For accurate wind power modeling, it may be of importance to consider the intra-farm variability of wind speed distribution. We found that when energy production exceeds 200MW, its estimation based on the averaged wind speed is significantly larger than when based on the speeds at individual turbines.

Characterization of the wind speed variability and future change in the Iberian Peninsula and the Balearic Islands

AbstractWind energy is susceptible to global climate change because it could alter the wind patterns. Then, improvement of our knowledge of wind field variability is crucial to optimize the use of wind resources in a given region.Here, we quantify the effects of climate change on the surface wind speed field over the Iberian Peninsula and Balearic Islands using an ensemble of four regional climate models driven by a global climate model.Regions of the Iberian Peninsula with coherent temporal variability in wind speed in each of the models are identified and analysed using cluster analysis. These regions are continuous in each model and exhibit a high degree of overlap across the models. The models forced by the European Reanalysis Interim (ERA‐Interim) reanalysis are validated against the European Climate Assessment and Dataset wind. We find that regional models are able to simulate with reasonable skill the spatial distribution of wind speed at 10 m in the Iberian Peninsula, identifying areas with common wind variability.Under the Special Report on Emissions Scenarios (SRES) A1B climate change scenario, the wind speed in the identified regions for 2031–2050 is up to 5% less than during the 1980–1999 control period for all models. The models also agree on the time evolution of spatially averaged wind speed in each region, showing a negative trend for all of them. These tendencies depend on the region and are significant at p = 5% or slightly more for annual trends, while seasonal trends are not significant in most of the regions and seasons. Copyright © 2015 John Wiley & Sons, Ltd.

Sensitivity of the air–sea CO<sub>2</sub> exchange in the Baltic Sea and Danish inner waters to atmospheric short-term variability

Abstract. Minimising the uncertainties in estimates of air–sea CO2 exchange is an important step toward increasing the confidence in assessments of the CO2 cycle. Using an atmospheric transport model makes it possible to investigate the direct impact of atmospheric parameters on the air–sea CO2 flux along with its sensitivity to, for example, short-term temporal variability in wind speed, atmospheric mixing height and atmospheric CO2 concentration. With this study, the importance of high spatiotemporal resolution of atmospheric parameters for the air–sea CO2 flux is assessed for six sub-basins within the Baltic Sea and Danish inner waters. A new climatology of surface water partial pressure of CO2 (pCO2w) has been developed for this coastal area based on available data from monitoring stations and on-board pCO2w measuring systems. Parameterisations depending on wind speed were applied for the transfer velocity to calculate the air–sea CO2 flux. Two model simulations were conducted – one including short-term variability in atmospheric CO2 (VAT), and one where it was not included (CAT). A seasonal cycle in the air–sea CO2 flux was found for both simulations for all sub-basins with uptake of CO2 in summer and release of CO2 to the atmosphere in winter. During the simulated period 2005–2010, the average annual net uptake of atmospheric CO2 for the Baltic Sea, Danish straits and Kattegat was 287 and 471 Gg C yr−1 for the VAT and CAT simulations, respectively. The obtained difference of 184 Gg C yr−1 was found to be significant, and thus ignoring short-term variability in atmospheric CO2 does have a sizeable effect on the air–sea CO2 exchange. The combination of the atmospheric model and the new pCO2w fields has also made it possible to make an estimate of the marine part of the Danish CO2 budget for the first time. A net annual uptake of 2613 Gg C yr−1 was found for the Danish waters. A large uncertainty is connected to the air–sea CO2 flux in particular caused by the transfer velocity parameterisation and the applied pCO2w climatology. However, as a significant difference of 184 Gg C yr−1 is obtained between the VAT and CAT simulations, the present study underlines the importance of including short-term variability in atmospheric CO2 concentration in future model studies of the air–sea exchange in order to minimise the uncertainty.

Temporal and Spatial Variability of Wind Resources in the United States as Derived from the Climate Forecast System Reanalysis

Abstract This study examines the spatial and temporal variability of wind speed at 80 m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the winter (summer), and higher (lower) speeds over much of the Midwest and U.S. Northeast (U.S. West and Southeast). Trends are also variable spatially, with more upward trends in areas of the Great Plains and Intermountain West of the United States and more downward trends elsewhere. The leading EOF mode, which accounts for 20% (summer) to 33% (winter) of the total variance and represents in-phase variations across the United States, responds mainly to the North Atlantic Oscillation (NAO) in summer and El Niño–Southern Oscillation (ENSO) in the other seasons. The dominant variation pattern can be explained by a southerly/southwesterly (westerly) anomaly over the U.S. East (U.S. West) as a result of the anomalous mean sea level pressure (MSLP) pattern. The second EOF mode, which explains about 15% of the total variance and shows a seesaw pattern, is mainly related to the springtime Arctic Oscillation (AO), the summertime recurrent circumglobal teleconnection (CGT), the autumn Pacific decadal oscillation (PDO), and the winter El Niño Modoki. The anomalous jet stream and MSLP patterns associated with these indices are responsible for the wind variation.

Sub‐kilometer dynamical downscaling of near‐surface winds in complex terrain using WRF and MM5 mesoscale models

Sub‐kilometer dynamical downscaling was performed using the Weather Research and Forecasting (WRF) and Mesoscale Model Version 5 (MM5) models. The models were configured with horizontal grid spacing ranging from 27 km in the outermost telescoping to 333 m in the innermost domains and verified with observations collected at four 50‐m towers in west‐central Nevada during July and December 2007. Moment‐based and spectral verification metrics showed that the performance of WRF was superior to MM5. The modeling results were more accurate at 50 m than at 10 m AGL. Both models accurately simulated the mean near‐surface wind shear; however, WRF (MM5) generally overestimated (underestimated) mean wind speeds at these levels. The dispersion errors were the dominant component of the root‐mean square errors. The major weakness of WRF was the overestimation of the intensity and frequency of strong nocturnal thermally driven flows and their sub‐diurnal scale variability, while the main weaknesses of MM5 were larger biases, underestimation of the frequency of stronger daytime winds in the mixed layer and underestimation of the observed spectral kinetic energy of the major energy‐containing motions. Neither of the verification metrics showed systematic improvement in the models' accuracy with increasing the horizontal resolution and the share of dispersion errors increased with increased resolution. However, a profound improvement in the moment‐based accuracy was found for the mean vertical wind shear and the temporal variability of wind speed, in particular for summer daytime simulations of the thermally driven flows. The most prominent spectral accuracy improvement among the primary energy‐containing frequency bands was found for both models in the summertime diurnal periods. Also, the improvement for WRF (MM5) was more (less) apparent for longer‐than‐diurnal than for sub‐diurnal periods. Finally, the study shows that at least near‐kilometer horizontal grid spacing is necessary for dynamical downscaling of near‐surface wind speed climate over complex terrain; however, some of the physics options might be less appropriate for grid spacing nearing the scales of the energy‐containing turbulent eddies, i.e., resolutions of several hundred meters. In addition to the effects of the lower boundary, the accuracy of the lateral boundary conditions of the parent domains also controls the onset and evolution of the thermally driven flows.

Wind variability in the northwestern part of the Black Sea from the offshore fixed platform observation data

The statistically ensured estimates of characteristics of temporal variability of wind speed and wind direction are obtained on the basis of observations carried out in 1996–2001 at the offshore fixed platform in the northwestern part of the Black Sea. The maximum values of monthly mean wind speed (more than 8 m/s) are registered in the cold half-year and the minimum ones (∼4 m/s), in summer. The moderate winds of northern and southern directions dominate during the whole year. Using the Pearson’s chi-squared test, it is demonstrated that the wind speed follows the normal distribution law during the most part of observation period. The largest deviations from the normal law are timed to the periods of existence of intensive large-scale anomalies in the ocean-atmosphere system. A significant alternation of both synoptic and lower-frequency intramonthly wind speed fluctuations with typical periods of ∼10–15 days is revealed. Their peak amplitude was registered in the fall and winter season of 1997/98, i.e., it was observed during the mature phase of one of the most intensive El Ninos during the whole period of instrumental observations. At that time, the energy of intramonthly low-frequency wind speed fluctuations (∼4 m2/s2) was equal to the energy of fluctuations within the synoptic range of the spectrum.

Decreasing wind speed and weakening latitudinal surface pressure gradients in the Tibetan Plateau

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 42:57-64 (2010) - DOI: https://doi.org/10.3354/cr00864 Decreasing wind speed and weakening latitudinal surface pressure gradients in the Tibetan Plateau Qinglong You1,2,6, Shichang Kang1,3,*, Wolfgang-Albert Flügel2, Nick Pepin4, Yuping Yan5, Jie Huang1,6 1Key Laboratory of Tibetan Environmental Change and Land Surface Process, Institute of Tibetan Plateau Research, Chinese Academy of Sciences (CAS), Beijing 100085, China 2Department of Geoinformatics, Friedrich-Schiller University Jena, Jena 07743, Germany 3State Key Laboratory of Cryospheric Science, CAS, Lanzhou 730000, China 4Department of Geography, University of Portsmouth, PO1 3HE, UK 5National Climate Center, Beijing 100081, China 6Graduate University of Chinese Academy of Sciences, Beijing 100049, China *Corresponding author. Email: shichang.kang@itpcas.ac.cn ABSTRACT: The Tibetan Plateau, with an average elevation of over 4000 m above sea level, is the highest and most extensive highland in the world. Between 1980 and 2005, the annual mean temperature has warmed at the rate of 0.38°C decade–1. However, little attention has been paid to the variation of wind speed, the most important factor controlling evapotranspiration in the Tibetan Plateau. Here we used monthly mean wind speed from the Chinese Meteorological Administration data set to examine the spatial and temporal variability of wind speed at 71 stations (with elevations above 2000 m above sea level) in the eastern and central Tibetan Plateau during 1980–2005, and compared wind speed climatology and their trends with NCEP and ERA-40 reanalyses in the same domain. Compared with surface stations, NCEP overestimates wind speed and ERA-40 underestimates it, with mean annual biases of 0.93 m s–1 for NCEP and –0.75 m s–1 for ERA-40. Both surface stations and NCEP reanalysis show significant decreasing trends, at rates of –0.24 and –0.13 m s–1 decade–1, respectively, mainly evident in spring and summer. ERA-40 fails to capture any decrease. The above results indicate that NCEP captures wind speed better than ERA-40. We speculate that the most likely cause of diminishing wind speed are the asymmetrically decreasing latitudinal surface temperature and pressure gradients over the Tibetan Plateau, which may be part of a large-scale atmospheric circulation shift. KEY WORDS: Tibetan Plateau · Wind speed · NCEP · ERA-40 · Pressure gradient Full text in pdf format PreviousNextCite this article as: You Q, Kang S, Flügel WA, Pepin N, Yan Y, Huang J (2010) Decreasing wind speed and weakening latitudinal surface pressure gradients in the Tibetan Plateau. Clim Res 42:57-64. https://doi.org/10.3354/cr00864 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 42, No. 1. Online publication date: May 27, 2010 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2010 Inter-Research.

Calculating long‐term global air‐sea flux of carbon dioxide using scatterometer, passive microwave, and model reanalysis wind data

Global air‐sea flux of carbon dioxide (CO2) is calculated from wind data acquired by the satellite scatterometer QuikSCAT, the passive microwave radiometer AMSR‐E, and the model reanalysis ERA‐40 using four of the most commonly used wind speed dependent parameterizations of gas transfer velocity. Assuming QuikSCAT as reference, the results are compared to obtain an estimate of that relative uncertainty in the flux calculations which results solely from the origin of the input wind data. We illustrate the discrepancies between these data sets and quantify the uncertainty in the computed air‐sea CO2 flux that arises from data processing such as temporal and spatial averaging using AMSR‐E as an example data set. The impact of temporal variability of wind speed is shown to be significantly greater than that of spatial variability. However, simple parameterizations of temporal variability are found to be sensor‐specific and cannot be applied in a straightforward way to data sets with lower temporal resolutions from other sensors. We show a simple methodology to correct monthly mean data in such a way that seasonally and zonally varying parameterizations of temporal variability derived from QuikSCAT data can be applied to data from AMSR‐E and ERA‐40. This allows us to produce a global 44‐year time series of gas transfer velocity and to present a more coherent estimate of air‐sea transfer of carbon dioxide from the three most commonly available types of wind data.

A numerical investigation of mechanisms affecting drainage flows in highly Complex Terrain

Numerical simulations of increasing complexity are conducted to investigate topographic controls and ambient wind effects upon drainage flows along a portion of the Colorado Front Range in the central Rocky Mountains. A series of two-dimensional simulations show the effects upon the drainage flow of changing slope gradient at the mountain-plain interface. For a given mountain slope, a decrease in the slope of the plain decelerates the mountain drainage jet as it approaches the plain and causes the jet to elevate. The integrated effects of slope and valley drainage are presented with particle trajectories for a particular drainage basin along the Front Range. A nested grid simulation of drainage flow from multiple basins along the Front Range shows that basin area is an important factor in the strength of the drainage flow and that canyon topography variations greatly affect the behavior of the drainage jet as it flows through the canyon mouth onto the plain. Strong drainage winds developed on each of four case night simulations due to the presence of only weak ambient wind below mountaintop. The weak winds represent a decoupling of the near-surface from stronger winds above mountaintop. The canyon drainage exhibited substantial temporal variability in wind speed with the inclusion of ambient winds, due to interactions between ambient and drainage winds.

Weather‐Based Variations in Thermal Stratification

A one-dimensional mixed-layer mathematical model of thermal stratification was used to evaluate the sensitivity of stratification conditions in a future reservoir to natural variations in attendant meteorological conditions. Weather data collected for 13 years at the National Weather Service (NWS) Station in Allentown, Pennsylvania, were included in the analysis. Wind speed, and the temporal variability in wind speed, were observed to be lower during summer. The simulated stratification regime was found to be extremely sensitive to meteorological conditions, as evidenced by the wide range of features of stratification that were projected for the 13 years of NWS data. The influence of the variability of wind from year to year was particularly evident in the broad ranges in the depth of the epilimnion in spring and fall, the temperature of the hypolimnion, and the duration of stratification. It is concluded that meteorological inputs need to be defined accurately for the development of credible stratification models, and that the influence of weather-based natural variability in stratification should be considered in the analysis of related environmental parameters.