Near-surface mean and gust wind speed in ERA5 across Sweden: towards an improved gust parametrization

<p>Driven by the twenty-century surface air temperature rise, extreme wind events could change in their frequency and magnitude of occurrence, with drastic impacts on human and ecosystems. As a matter of fact, windstorms and extreme wind conditions contribute to more than half of the economic losses associated with natural disasters in Europe. Across Scandinavia, the occurrence of wind gust events can affect aviation security, as well as damage buildings and forests, representing severe hazards to people, properties and transport. Comprehensive extreme wind datasets and analysis can help improving our understanding of these changes and help the society to cope with these changes. Unfortunately, due to the difficulty in measuring wind gust and the lack of homogeneous and continuous datasets across Sweden, it is challenging to assess and attribute their changes. Global reanalysis products represent a potential tool for assessing changes and impact of extreme winds, only if their ability in representing observed near-surface wind statistics can be demonstrated.</p><p>In this study the new ERA5 reanalysis product has been compared with hourly near-surface wind speed and gust observations across Sweden for 2013-2017. We found that ERA5 shows better agreement with both mean wind speed and gust measurements compared to the previous ERA-Interim reanalysis dataset. Especially across coastal regions, ERA5 has a closer agreement with observed climate statistics. However, significant discrepancies are still found for inland and high-altitude regions. Therefore, the gust parametrization used in ERA5 is further analyzed to better understand if the adopted gust formulation matches the physical processes behind the gust occurrence. Finally, an improved formulation of the gust parametrization is developed across Sweden and further tested for Norway, which is characterized by more complex topography.</p>

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Hurricane Surface Wind Measurements from an Operational Stepped Frequency Microwave Radiometer

Monthly Weather Review ◽

10.1175/mwr3454.1 ◽

2007 ◽

Vol 135 (9) ◽

pp. 3070-3085 ◽

Cited By ~ 192

Author(s):

Eric W. Uhlhorn ◽

Peter G. Black ◽

James L. Franklin ◽

Mark Goodberlet ◽

James Carswell ◽

...

Keyword(s):

Wind Speed ◽

Drag Coefficient ◽

Surface Wind ◽

Surface Wind Speed ◽

Model Function ◽

Near Surface ◽

Wind Speeds ◽

Extreme Wind ◽

High Bias ◽

Stepped Frequency

Abstract For the first time, the NOAA/Aircraft Operations Center (AOC) flew stepped frequency microwave radiometers (SFMRs) on both WP-3D research aircraft for operational hurricane surface wind speed measurement in 2005. An unprecedented number of major hurricanes provided ample data to evaluate both instrument performance and surface wind speed retrieval quality up to 70 m s−1 (Saffir–Simpson category 5). To this end, a new microwave emissivity–wind speed model function based on estimates of near-surface winds in hurricanes by global positioning system (GPS) dropwindsondes is proposed. For practical purposes, utilizing this function removes a previously documented high bias in moderate SFMR-measured wind speeds (10–50 m s−1), and additionally corrects an extreme wind speed (>60 m s−1) underestimate. The AOC operational SFMRs yield retrievals that are precise to within ∼2% at 30 m s−1, which is a factor of 2 improvement over the NOAA Hurricane Research Division’s SFMR, and comparable to the precision found here for GPS dropwindsonde near-surface wind speeds. A small (1.6 m s−1), but statistically significant, overall high bias was found for independent SFMR measurements utilizing emissivity data not used for model function development. Across the range of measured wind speeds (10–70 m s−1), SFMR 10-s averaged wind speeds are within 4 m s−1 (rms) of the dropwindsonde near-surface estimate, or 5%–25% depending on speed. However, an analysis of eyewall peak wind speeds indicates an overall 2.6 m s−1 GPS low bias relative to the peak SFMR estimate on the same flight leg, suggesting a real increase in the maximum wind speed estimate due to SFMR’s high-density sampling. Through a series of statistical tests, the SFMR is shown to reduce the overall bias in the peak surface wind speed estimate by ∼50% over the current flight-level wind reduction method and is comparable at extreme wind speeds. The updated model function is demonstrated to behave differently below and above the hurricane wind speed threshold (∼32 m s−1), which may have implications for air–sea momentum and kinetic energy exchange. The change in behavior is at least qualitatively consistent with recent laboratory and field results concerning the drag coefficient in high wind speed conditions, which show a fairly clear “leveling off” of the drag coefficient with increased wind speed above ∼30 m s−1. Finally, a composite analysis of historical data indicates that the earth-relative SFMR peak wind speed is typically located in the hurricane’s right-front quadrant, which is consistent with previous observational and theoretical studies of surface wind structure.

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The Analysis of the Spatiotemporal Variations and Mechanisms for the Near-Surface Wind Speed Over China in the Last 40 Years

10.21203/rs.3.rs-932142/v1 ◽

2021 ◽

Author(s):

Xia Li ◽

Yongjie Pan ◽

Yingsha Jiang

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

The Other ◽

Strong Wind ◽

Reanalysis Dataset ◽

Near Surface ◽

Wind Speeds ◽

Dispersion Degree ◽

Weak Wind

Abstract Near-surface wind speed is of great significance in many aspects of the human production and living. This study analyses the spatiotemporal characteristics of the near-surface wind speed and wind speed percentiles with meteorological station observations in China from 1979 to 2019. Furthermore, the mechanisms of the wind speed variations are also investigated with ERA-Interim reanalysis dataset. Spatially, the wind speeds in the northern and eastern regions of China are larger than that in the central and southern regions. Seasonally, the wind speed in spring is significantly larger than that in the other seasons. The dispersion degree of wind speed in spring is larger than that in the other seasons both spatially and temporally. The near-surface wind speed in China shows significantly decreasing trends during 1979–2019, particularly in 1979–1999, but the wind speed trend reversed after 2000. After dividing the wind speed into different percentiles, it recognizes that the decreasing trend of stronger winds are more significant than that of weaker winds. The weaker the wind speed, the more significant increasing trend after 2000. Therefore, the decreasing wind speed trend before 2000 is mainly caused by the significant reduction of strong wind, while the reversal trend after 2000 results from the increase of weak wind. The variations of the wind speed over China attributed to both the U and V wind components, and the variations of zonal wind is closely related to the weakened upper westerly wind field and the uneven warming between high and low latitudes.

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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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