The Influence of Boundary Layer Processes on the Diurnal Variation of the Climatological Near-Surface Wind Speed Probability Distribution over Land*

Abstract Knowledge of the diurnally varying land surface wind speed probability distribution is essential for surface flux estimation and wind power management. Global observations indicate that the surface wind speed probability density function (PDF) is characterized by a Weibull-like PDF during the day and a nighttime PDF with considerably greater skewness. Consideration of long-term tower observations at Cabauw, the Netherlands, indicates that this nighttime skewness is a shallow feature connected to the formation of a stably stratified nocturnal boundary layer. The observed diurnally varying vertical structure of the leading three climatological moments of near-surface wind speed (mean, standard deviation, and skewness) and the wind power density at the Cabauw site can be successfully simulated using the single-column version of the Canadian Centre for Climate Modelling and Analysis (CCCma) fourth-generation atmospheric general circulation model (CanAM4) with a new semiempirical diagnostic turbulent kinetic energy (TKE) scheme representing downgradient turbulent transfer processes for cloud-free conditions. This model also includes a simple stochastic representation of intermittent turbulence at the boundary layer inversion. It is found that the mean and the standard deviation of wind speed are most influenced by large-scale “weather” variability, while the shape of the PDF is influenced by the intermittent mixing process. This effect is quantitatively dependent on the asymptotic flux Richardson number, which determines the Prandtl number in stable flows. High vertical resolution near the land surface is also necessary for realistic simulation of the observed fine vertical structure of wind speed distribution.

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Wind Speed Forecast Based on Post-Processing of Numerical Weather Predictions Using a Gradient Boosting Decision Tree Algorithm

Atmosphere ◽

10.3390/atmos11070738 ◽

2020 ◽

Vol 11 (7) ◽

pp. 738 ◽

Cited By ~ 1

Author(s):

Wenqing Xu ◽

Like Ning ◽

Yong Luo

Keyword(s):

Wind Speed ◽

Decision Tree ◽

Wind Power ◽

Surface Wind ◽

Surface Wind Speed ◽

Gradient Boosting ◽

Post Processing ◽

Near Surface ◽

Wind Power Forecasting ◽

Numerical Weather

With the large-scale development of wind energy, wind power forecasting plays a key role in power dispatching in the electric power grid, as well as in the operation and maintenance of wind farms. The most important technology for wind power forecasting is forecasting wind speed. The current mainstream methods for wind speed forecasting involve the combination of mesoscale numerical meteorological models with a post-processing system. Our work uses the WRF model to obtain the numerical weather forecast and the gradient boosting decision tree (GBDT) algorithm to improve the near-surface wind speed post-processing results of the numerical weather model. We calculate the feature importance of GBDT in order to find out which feature most affects the post-processing wind speed results. The results show that, after using about 300 features at different height and pressure layers, the GBDT algorithm can output more accurate wind speed forecasts than the original WRF results and other post-processing models like decision tree regression (DTR) and multi-layer perceptron regression (MLPR). Using GBDT, the root mean square error (RMSE) of wind speed can be reduced from 2.7–3.5 m/s in the original WRF result by 1–1.5 m/s, which is better than DTR and MLPR. While the index of agreement (IA) can be improved by 0.10–0.20, correlation coefficient be improved by 0.10–0.18, Nash–Sutcliffe efficiency coefficient (NSE) be improved by −0.06–0.6. It also can be found that the feature which most affects the GBDT results is the near-surface wind speed. Other variables, such as forecast month, forecast time, and temperature, also affect the GBDT results.

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Model-Based Projections and Uncertainties of Near-Surface Wind Climate in Western Canada

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-16-0091.1 ◽

2016 ◽

Vol 55 (10) ◽

pp. 2229-2245 ◽

Cited By ~ 5

Author(s):

Jeffrey T. Daines ◽

Adam H. Monahan ◽

Charles L. Curry

Keyword(s):

Power Generation ◽

Standard Deviation ◽

Wind Speed ◽

Wind Power ◽

Surface Wind ◽

Western Canada ◽

Near Surface ◽

Wind Speeds ◽

Ensemble Mean ◽

Wind Climate

AbstractNear-surface wind is important in forestry, agriculture, air pollution, building energy use, and wind power generation. In western Canada it presently plays a minor role in power generation, but ongoing reductions in the cost of wind power infrastructure and the increasing costs of conventional power generation (including environmental costs) motivate the assessment of the projected future wind climate and uncertainties in this projection. Multiple realizations of the Canadian Regional Climate Model (CRCM) at 45-km resolution were driven by two global climate models over the periods 1971–2000 (using historical greenhouse gas concentrations) and 2031–60 (using the SRES-A2 concentration scenario). Hourly wind speeds from 30 stations were analyzed over 1971–2000 and used to calibrate downscaled ensembles of projected wind speed distributions over 2031–60. At most station locations modest increases in mean wind speed were found for a majority of the projections, but with an ensemble spread of the same order of magnitude as the increases. Relative changes in mean wind speeds at station locations were found to be insensitive to the station observations and calibration technique. In view of this result, projected relative changes in future wind climate over the entire CRCM domain were estimated using uncalibrated pairs of past-period and future-period wind speed distributions. The relative changes are robust, in the sense that their ensemble mean relative change is greater than their standard deviation, but are not very substantial, in the sense that their ensemble mean change is generally less than the standard deviation of their annual means.

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Influence of a Hailstreak on Boundary Layer Evolution

Monthly Weather Review ◽

10.1175/mwr2897.1 ◽

2005 ◽

Vol 133 (4) ◽

pp. 942-960 ◽

Cited By ~ 9

Author(s):

Zewdu T. Segele ◽

David J. Stensrud ◽

Ian C. Ratcliffe ◽

Geoffrey M. Henebry

Keyword(s):

Boundary Layer ◽

Wind Speed ◽

South Dakota ◽

Land Surface ◽

Surface Wind ◽

Sea Breeze ◽

Surface Wind Speed ◽

Vegetation Coverage ◽

Wind Speeds ◽

Severe Thunderstorms

Severe thunderstorms developed on 20 June 1997 and produced heavy precipitation, damaging winds, and large hail over two swaths in southeastern South Dakota. Calculations of fractional vegetation coverage (scaled from 0 to 1) based upon composite satellite data indicate that, within the hailstreak region, vegetation coverage decreased from 0.50 to near 0.25 owing to the damaging effects of hail on the growing vegetation. The northern edge of the larger hailstreak was located a few kilometers south of Chamberlain, South Dakota, a National Weather Service surface observation site. Hourly observations from Chamberlain and several nearby surface sites in South Dakota are averaged over 7 days both before and after this hail event. These observations illustrate that the late-afternoon (nighttime) temperatures are 2°C higher (2°C lower) near the hailstreak after the event than before the event. Similarly, daily average dewpoint temperatures after the event are 2.6°C lower near the hailstreak. These changes are consistent with the influences of a recently devegetated zone on changes to the surface energy budget. To explore how these hailstreaks further affected the evolution of the planetary boundary layer in this region, two model simulations are performed using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5). In the control run, climatology is used for the land surface characteristics, and hence the simulation is independent of the hailstreaks. In the hailstreak simulation (HSS), the fractional vegetation coverage and soil moisture in the hailstreak regions are modified to reflect the likely conditions within the hailstreaks. Two different days are simulated: one with low surface wind speeds and one with stronger surface wind speeds. For the low surface wind speed case, the HSS simulation produces a sea-breeze-like circulation in the boundary layer by midmorning. For the stronger surface wind speed case, this sea-breeze-like circulation does not develop in the HSS, but the simulated low-level temperatures are modified over a larger area. These results suggest that to capture and reasonably simulate the evolution of boundary layer structures, there is a need for routine daily updates of land surface information. Hailstreaks also are important to consider in the future as the focus for observational studies on nonclassical mesoscale circulations.

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The Probability Distribution of Sea Surface Wind Speeds. Part II: Dataset Intercomparison and Seasonal Variability

Journal of Climate ◽

10.1175/jcli3641.1 ◽

2006 ◽

Vol 19 (4) ◽

pp. 521-534 ◽

Cited By ~ 49

Author(s):

Adam Hugh Monahan

Keyword(s):

Standard Deviation ◽

Wind Speed ◽

Probability Distribution ◽

Surface Wind ◽

Surface Wind Speed ◽

Sea Surface ◽

Leading Order ◽

Statistical Structure ◽

Wind Speeds ◽

Sea Surface Wind

Abstract The statistical structure of sea surface wind speeds is considered, both in terms of the leading-order moments (mean, standard deviation, and skewness) and in terms of the parameters of a best-fit Weibull distribution. An intercomparison is made of the statistical structure of sea surface wind speed data from four different datasets: SeaWinds scatterometer observations, a blend of Special Sensor Microwave Imager (SSM/I) satellite observations with ECMWF analyses, and two reanalysis products [NCEP–NCAR and 40-yr ECMWF Re-Analysis (ERA-40)]. It is found that while the details of the statistical structure of sea surface wind speeds differs between the datasets, the leading-order features of the distributions are consistent. In particular, it is found in all datasets that the skewness of the wind speed is a concave upward function of the ratio of the mean wind speed to its standard deviation, such that the skewness is positive where the ratio is relatively small (such as over the extratropical Northern Hemisphere), the skewness is close to zero where the ratio is intermediate (such as the Southern Ocean), and the skewness is negative where the ratio is relatively large (such as the equatorward flank of the subtropical highs). This relationship between moments is also found in buoy observations of sea surface winds. In addition, the seasonal evolution of the probability distribution of sea surface wind speeds is characterized. It is found that the statistical structure on seasonal time scales shares the relationships between moments characteristic of the year-round data. Furthermore, the seasonal data are shown to depart from Weibull behavior in the same fashion as the year-round data, indicating that non-Weibull structure in the year-round data does not arise due to seasonal nonstationarity in the parameters of a strictly Weibull time series.

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Near-Surface Wind-Speed Stilling in Alaska during 1984-2016 and Its Impact on the Sustainability of Wind Power

Journal of Power and Energy Engineering ◽

10.4236/jpee.2019.77006 ◽

2019 ◽

Vol 07 (07) ◽

pp. 71-124

Author(s):

Gerhard Kramm ◽

Nicole Mölders ◽

John Cooney ◽

Ralph Dlugi

Keyword(s):

Wind Speed ◽

Wind Power ◽

Surface Wind ◽

Surface Wind Speed ◽

Near Surface

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Recurrence Characteristics Analysis of Near-Surface Wind Speed Signal with Different Sampling Frequencies

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1605 ◽

2014 ◽

Vol 599-601 ◽

pp. 1605-1609 ◽

Cited By ~ 1

Author(s):

Ming Zeng ◽

Zhan Xie Wu ◽

Qing Hao Meng ◽

Jing Hai Li ◽

Shu Gen Ma

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

Recurrence Plot ◽

Sampling Frequency ◽

Near Surface ◽

Gas Leakage ◽

Time Period ◽

Characteristics Analysis ◽

Quantification Analysis

The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer.

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