Methodology for obtaining wind gusts using Doppler lidar

A new methodology is proposed for scaling Doppler lidar observations of wind gusts to make them comparable with those observed at a meteorological mast. Doppler lidars can then be used to measure wind gusts in regions and heights where traditional meteorological mast measurements are not available. This novel method also provides estimates for wind gusts at arbitrary gust durations, including those shorter than the temporal resolution of the Doppler lidar measurements. The input parameters for the scaling method are the measured wind‐gust speed as well as the mean and standard deviation of the horizontal wind speed. The method was tested using WindCube V2 Doppler lidar measurements taken next to a 100 m high meteorological mast. It is shown that the method can provide realistic Doppler lidar estimates of the gust factor, i.e. the ratio of the wind‐gust speed to the mean wind speed. The method reduced the bias in the Doppler lidar gust factors from 0.07 to 0.03 and can be improved further to reduce the bias by using a realistic estimate of turbulence. Wind gust measurements are often prone to outliers in the time series, because they represent the maximum of a (moving‐averaged) horizontal wind speed. To assure the data quality in this study, we applied a filtering technique based on spike detection to remove possible outliers in the Doppler lidar data. We found that the spike detection‐removal method clearly improved the wind‐gust measurements, both with and without the scaling method. Spike detection also outperformed the traditional Doppler lidar quality assurance method based on carrier‐to‐noise ratio, by removing additional unrealistic outliers present in the time series.

[1]  K. A. Bekiashev,et al.  World Meteorological Organization (WMO) , 2018, Yearbook of International Cooperation on Environment and Development 1998–99.

[2]  Jörg Hartmann,et al.  Gust factor based on research aircraft measurements: a new methodology applied to the Arctic marine boundary layer , 2016 .

[3]  Alfredo Peña,et al.  Weibull Wind-Speed Distribution Parameters Derived from a Combination of Wind-Lidar and Tall-Mast Measurements Over Land, Coastal and Marine Sites , 2016, Boundary-Layer Meteorology.

[4]  Christopher Jung,et al.  The Role of Highly-Resolved Gust Speed in Simulations of Storm Damage in Forests at the Landscape Scale: A Case Study from Southwest Germany , 2016 .

[5]  Ville Vakkari,et al.  A generalised background correction algorithm for a Halo Doppler lidar and its application to data from Finland , 2015 .

[6]  Ronny Leinweber,et al.  An assessment of the performance of a 1.5 μm Doppler lidar for operational vertical wind profiling based on a 1-year trial , 2015 .

[7]  M. Lexer,et al.  Developing predictive models of wind damage in Austrian forests , 2015, Annals of Forest Science.

[8]  Diofantos G. Hadjimitsis,et al.  Low-level mixing height detection in coastal locations with a scanning Doppler lidar , 2014 .

[9]  C. Fortelius,et al.  On the vertical structure of wind gusts , 2014 .

[10]  J. Mann,et al.  A review of turbulence measurements using ground-based wind lidars , 2013 .

[11]  Janet F. Barlow,et al.  An assessment of a three-beam Doppler lidar wind profiling method for use in urban areas , 2013 .

[12]  Timo Vihma,et al.  Wind‐gust parametrizations at heights relevant for wind energy: a study based on mast observations , 2013 .

[13]  Alfredo Peña,et al.  Observations of the atmospheric boundary layer height under marine upstream flow conditions at a coastal site , 2013 .

[14]  J. Mann,et al.  How good are remote sensors at measuring extreme winds , 2011 .

[15]  E. O'connor,et al.  A Method for Estimating the Turbulent Kinetic Energy Dissipation Rate from a Vertically Pointing Doppler Lidar, and Independent Evaluation from Balloon-Borne In Situ Measurements , 2010 .

[16]  Guy N. Pearson,et al.  An Analysis of the Performance of the UFAM Pulsed Doppler Lidar for Observing the Boundary Layer , 2009 .

[17]  R. Banta,et al.  Boundary-layer anemometry by optical remote sensing for wind energy applications , 2007 .

[18]  S. Larsen,et al.  On the extension of the wind profile over homogeneous terrain beyond the surface boundary layer , 2007 .

[19]  M. Parlange,et al.  On the Parameterization of Surface Roughness at Regional Scales , 2005 .

[20]  J. W. Verkaik Evaluation of Two Gustiness Models for Exposure Correction Calculations , 2000 .

[21]  Dean Vickers,et al.  Quality Control and Flux Sampling Problems for Tower and Aircraft Data , 1997 .

[22]  J. Wieringa,et al.  Does representative wind information exist , 1996 .

[23]  Per Capita,et al.  About the authors , 1995, Machine Vision and Applications.

[24]  J. Højstrup A statistical data screening procedure , 1993 .

[25]  I. Troen,et al.  In search of a gust definition , 1991 .

[26]  A. Beljaars,et al.  The Influence of Sampling and Filtering on Measured Wind Gusts , 1987 .

[27]  J. Wieringa Roughness‐dependent geographical interpolation of surface wind speed averages , 1986 .

[28]  J. Wieringa,et al.  Gust factors over open water and built-up country , 1973 .

[29]  J. Kaimal,et al.  Spectral Characteristics of Surface-Layer Turbulence , 1972 .

[30]  S. Rice Mathematical analysis of random noise , 1944 .

[31]  S. Gryning,et al.  Ten Years of Boundary-Layer and Wind-Power Meteorology at Høvsøre, Denmark , 2015, Boundary-Layer Meteorology.

[32]  Corinna Rebmann,et al.  Data Acquisition and Flux Calculations , 2012 .

[33]  Gertie Geertsema,et al.  Theory for a TKE based parameterization of wind gusts , 2008 .

[34]  O. Brasseur Development and application of a physical approach to estimating wind gusts , 2001 .

[35]  T. Vihma,et al.  On the effective roughness length for heterogeneous terrain , 1991 .