Observations and simulations of a wind farm modifying a thunderstorm outflow boundary

Abstract. On 18 June 2019, National Weather Service (NWS) radar reflectivity data indicated the presence of thunderstorm-generated outflow propagating east-southeastward near Lubbock, Texas. A section of the outflow boundary encountered a wind farm and then experienced a notable reduction in ground-relative velocity, suggesting that interactions with the wind farm impacted the outflow boundary progression. We use the Weather Research and Forecasting model and its wind farm parameterization to address the extent to which wind farms can modify the near-surface environment of thunderstorm outflow boundaries. We conduct two simulations of the June 2019 outflow event: one containing the wind farm and one without. We specifically investigate the outflow speed of the section of the boundary that encounters the wind farm and the associated impacts on near-surface wind speed, moisture, temperature, and changes to precipitation features as the storm and associated outflow pass over the wind farm domain. The NWS radar and nearby West Texas Mesonet surface stations provide observations for validation of the simulations. The presence of the wind farm in the simulation clearly slows the progress of the outflow boundary by over 20 km h−1, similar to what was observed. Simulated perturbations of surface wind speed, temperature, and moisture associated with outflow passage were delayed by up to 6 min when the wind farm was present in the simulation compared to the simulation without the wind farm. However, impacts on precipitation were localized and transient, with no change to total accumulation across the domain.

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Observations and Simulations of a Wind Farm Modifying a Thunderstorm Outflow Boundary

10.5194/wes-2020-69 ◽

2020 ◽

Author(s):

Jessica M. Tomaszewski ◽

Julie K. Lundquist

Keyword(s):

Wind Speed ◽

Wind Farm ◽

Surface Wind ◽

Wind Farms ◽

Propagation Speed ◽

Surface Wind Speed ◽

Near Surface ◽

Thunderstorm Outflow ◽

Reflectivity Data ◽

Outflow Boundary

Abstract. On June 18, 2019, National Weather Service (NWS) radar reflectivity data indicated the presence of thunderstorm-generated outflow propagating east-southeast near Lubbock, Texas. A section of the outflow boundary encountered a wind farm, and then experienced a notable reduction in propagating speed, suggesting that interactions with the wind farm impacted the outflow boundary progression. We use the Weather Research and Forecasting model and its Wind Farm Parameterization to address the extent to which wind farms can modify thunderstorm outflow boundaries. We conduct two simulations of the June 2019 outflow event, one containing the wind farm and one without. We specifically investigate the outflow propagation speed of the section of the boundary that encounters the wind farm and the associated impacts to near-surface wind speed, moisture, temperature, and changes to precipitation features as the storm and associated outflow pass over the wind farm domain. The NWS radar and nearby West Texas Mesonet surface stations provide observations for validation of the simulations. The presence of the wind farm in the simulation clearly slows the progress of the outflow boundary by over 20 km hr−1 similar to what was observed. Simulated perturbations of surface wind speed, temperature, and moisture associated with outflow passage were delayed by up to 6 minutes when the wind farm was present in the simulation compared to the simulation without the wind farm. However, impacts to precipitation were localized and transient, with no change to total accumulation across the domain.

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Effects of wind farms on near-surface wind speed

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/233/5/052041 ◽

2019 ◽

Vol 233 ◽

pp. 052041

Author(s):

Zhang Xin ◽

Yin Ruiping ◽

Ronghui Xu ◽

Liu Jing ◽

He Jingli ◽

...

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Wind Farms ◽

Surface Wind Speed ◽

Near Surface

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Local effects of wind turbines on near-surface wind speed in a desert steppe wind farm

Journal of Earth System Science ◽

10.1007/s12040-020-01404-1 ◽

2020 ◽

Vol 129 (1) ◽

Author(s):

Dandan Zhou ◽

Shengrong Hu ◽

Xiaohong Dang ◽

Jinrong Li ◽

Chunxing Hai ◽

...

Keyword(s):

Wind Speed ◽

Wind Turbines ◽

Wind Farm ◽

Surface Wind ◽

Surface Wind Speed ◽

Desert Steppe ◽

Local Effects ◽

Near Surface

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Evaluation of Near-Surface Wind Speed Changes during 1979 to 2011 over China Based on Five Reanalysis Datasets

Atmosphere ◽

10.3390/atmos10120804 ◽

2019 ◽

Vol 10 (12) ◽

pp. 804 ◽

Cited By ~ 1

Author(s):

Jiang Yu ◽

Tianjun Zhou ◽

Zhihong Jiang ◽

Liwei Zou

Keyword(s):

Wind Speed ◽

Linear Trend ◽

Surface Wind ◽

Wind Farms ◽

Surface Wind Speed ◽

Resource Assessment ◽

Reanalysis Data ◽

Department Of Energy ◽

Near Surface ◽

Wind Resource

Wind speed data derived from reanalysis datasets has been used in the plan and design of wind farms in China, but the quality of these kinds of data over China remains unknown. In this study, the performances of five sets of reanalysis data, including National Centers for Environmental Predictions (NCEP)-U.S. Department of Energy (DOE) Reanalysis 2 (NCEP-2), Modern-ERA Retrospective Analysis for Research and Applications (MERRA), Japanese 55-year Reanalysis Project (JRA-55), Interim ECMWF Re-Analysis product (ERA-Interim), and 20th Century Reanalysis (20CR) in reproducing the climatology, interannual variation, and long-term trend of near-surface (10 m above ground) wind speed, for the period of 1979–2011 over continental China are comprehensively evaluated. Compared to the gridded data compiled from meteorological stations, all five reanalysis datasets reasonably reproduce the spatial distribution of the climatology of near-surface wind speed, but underestimate the intensity of the near-surface wind speed in most regions except for Tibetan Plateau where the wind speed is overestimated. All five reanalysis datasets show large weaknesses in reproducing the annual cycle of near-surface wind speed averaged over the continental China. The near-surface wind speed derived from the observations exhibit significant decreasing trends over most parts of continental China during 1979 to 2011. Although the spatial patterns of the linear trends reproduced by reanalysis datasets are close to the observation, the magnitudes are weaker in annual, spring, summer and autumn season. The qualities of all reanalysis datasets are limited in winter. For the interannual variability, except for winter, all five reanalysis datasets reasonably reproduce the interannual standard deviation but with larger amplitude. Quantitative comparison indicates that among the five reanalysis datasets, the MERRA (JRA-55) shows the relatively highest (lowest) skill in terms of the climatology and linear trend. These results call for emergent needs for developing high quality reanalysis data that can be used in wind resource assessment and planning.

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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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Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data

Renewable Energy ◽

10.1016/j.renene.2018.04.019 ◽

2018 ◽

Vol 126 ◽

pp. 1043-1054 ◽

Cited By ~ 43

Author(s):

Victoria Hoolohan ◽

Alison S. Tomlin ◽

Timothy Cockerill

Keyword(s):

Wind Speed ◽

Gaussian Process ◽

Meteorological Data ◽

Surface Wind ◽

Gaussian Process Regression ◽

Surface Wind Speed ◽

Near Surface ◽

Numerical Weather

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A preliminary study on the influence of Beijing urban spatial morphology on near-surface wind speed

Urban Climate ◽

10.1016/j.uclim.2020.100703 ◽

2020 ◽

Vol 34 ◽

pp. 100703

Author(s):

Yonghong Liu ◽

Yongming Xu ◽

Fangmin Zhang ◽

Wenjun Shu

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

Near Surface ◽

Spatial Morphology ◽

Preliminary Study

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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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