Badger, Jake

Clausen, Niels-Erik

Hahmann, Andrea N.

Larsén, Xiaoli Guo

Mann, Jakob

Ott, Søren

Online Research Database In Technology

                       General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal   If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.     Downloaded from orbit.dtu.dk on: Dec 18, 2017Recipe for correcting the effect of mesoscale resolution on the estimation of extremewindsLarsén, Xiaoli Guo; Ott, Søren; Badger, Jake; Hahmann, Andrea  N.; Mann, Jakob; Clausen, Niels-ErikPublished in:Scientific ProceedingsPublication date:2011Document VersionPublisher's PDF, also known as Version of recordLink back to DTU OrbitCitation (APA):Larsén, X. G., Ott, S., Badger, J., Hahmann, A. N., Mann, J., & Clausen, N-E. (2011). Recipe for correcting theeffect of mesoscale resolution on the estimation of extreme winds. In Scientific Proceedings (pp. 213-216).European Wind Energy Association (EWEA). For the mesoscale modeled winds, the spectral energy deficit in the mesoscalerange reflects the smoothing effect of both spatial and temporal resolution. Thisenergy deficit is essential in the extreme wind underestimation. Both approaches give consistent estimates in the smoothing effect in the peakfactor, with the first approach more straightforward in handling the combined spatialand temporal smoothing effect. The estimation of the smoothing effect in peak factor using the recipe is in goodagreement with measurements. For the offshore site Horns Rev, the conversion tothe mean annual wind maximum is successful according to the data validation.The mean kp calculated from 10 min wind time series at six stations is 5.07.kp and from 10 min measurement at Horns Rev are 4.96 and 27.2 m/s.The corresponding peak factors and mean wind maxima corrected from thesimulated hourly data using approach I are given in Table II and they are in goodagreement with measurement. These numbers fit rather well with the estimationgiven by approach II (Table II).1. Larsén X. G. and Mann J. The effects of disjunct sampling and averaging time on mean maximum wind  J. Wind Eng. Ind. Aerodyn., 94, 581-602, 2006.2. Skamarock W. Evaluating mesoscale NWP models using kinetic energy spectra. Monthly Weather Review, 132, 3019 – 3032, 2004.Recipe for correcting the effect of mesoscale resolution on the estimation of extreme windsXiaoli Guo Larsén, Søren Ott, Jake Badger, Andrea N. Hahmann, Jakob Mann, Niels-Erik ClausenWind Energy Division, Risø National Laboratory for Sustainable Energy, Technical University of DenmarkPO. ID456ReferencesEWEA 2011, Brussels, Belgium:  Europe’s Premier Wind Energy EventIt is a common issue that the modeled winds from mesoscale models are smeareddue to the spatial and temporal resolutions. This is reflected in the spectrumdomain as an energy deficit in the mesoscale range. The energy deficit indicatessmaller moments and thus underestimation in the extreme winds. We developedtwo approaches for calculating the smoothing effect due to the mesoscaleresolution on the extreme wind estimation by taking into account of the differenceof the spectral tail between the modeled and measured spectra in the mesoscalerange. Both approaches give the estimation of the smoothing effect in goodagreement with measurements from several sites in Denmark and Germany.The purpose is to develope approaches to take into account the difference in themeasured and simuated spectrum in the mesoscale range and to estimate theimpact from this difference on the extreme wind, in connection with the AnnualMaximum Method. The correction by the approaches will bridge the gap betweenthe current models’ resolution (tens of km or in hours) to that of the upper limit ofthe mesoscale range (a few km or in 10 min).Approach I: Spectral CorrectionThe wind time series is assumed to be a Gaussian process and the exceedancefollowing a Possion process at a large thresold. The peak factor kp , defined asis derived as a function of the spectral moments m0 and m2:with the moments defined as= 2 f, and ( )=sin( Ta/2)/( Ta/2) is a filter due to temporal averaging Ta. Thewind variation in high frequency range contributes significantly to m2/m0 andhence kp and the mean annual wind maximum .The core of this recipe is to replace the spectral tail of the simulated winds (thedots in Fig. 2) with a slope of -5/3 and extend it to f = 72 day-1(solid lines in Fig. 2with the tail start at f = 1 and 2 day-1 respectively).Approach II: Effective Temporal AveragingWe approximate the combined spatial and temporal averaging effect in themesoscale modeled winds into the temporal effect and use the statistical modelderived in [1] to calculate the underestimation in the extreme wind (see Fig. 3).The model from [1] assumes the wind time series a Gaussian process and itcalculates the temporal resolution effect on the annual wind maxima. The peakfactor is a function of the autocorrelation coefficient :where N is the number of 10 min values in a year. Both N and decrease withincreasing averaging time, so does kpt.Drawback: there is not always a good approximation for the combined effect as atemporal effect, e.g. REMO and WRF spectra in Fig. 3. Thus the estimation of thesmoothing effect is rather in a range (Table II), which means larger uncertainty.The problemAs illustrated in Fig. 1, due to the smoothing effect of resolution, there is an energydeficit in the mesoscale range, here ~ 2 < f < 72 day-1, in the spectra of thesimulated wind time series compared to that of the measurements. The spectraltail from measurements has a slope of approximately -5/3, and those from modelshave slopes -3 and -4. This implies that the extreme winds are underestimated inthe mesoscale modeling because the wind variation in this range is important incontributing high winds and hence in the peak effect.Simulations from three well-used mesoscale models(HIRHAM, REMO and WRF) for wind energy study inNorthern Europe are analyzed, see details in Table I.Table I: Model descriptions. All simulations give hourly outputs.Fig. 1: Spectra of wind speed at 10 m at Horns Rev, measurements and six model simulations. AbstractmaxUFig. 2: Modification of the spectrum of hourly simulated winds by replacing the tail for f > fc, here fc =1 and 2 day-1 up to 72 day-1.  (Approach I)Fig. 3: Lines show the spectrum from measurements varying with averaging time from 10 min to 6 hours. Also shown are six spectra from simulations. (Approach II) PurposeAcknowledgement: This work is supported by the Nordic project Climate and Energy System (1115063-01), Danish Grant for Mesoscale extreme winds (2009-1-10240)  and EU SafeWind project (no. 213740). Data from Horns Rev are provided by DONG Energy and REMO data by Max Planck Institute in Hamburg, Germany.Table II For the site Horns Rev. kp(I) are peak factors calculated from the simulated hourly wind time series. kp(II) are peak factor calculated with the modified spectrum shown in Fig. 2, equivalent to 10 min. SE, the smoothing effect calculated with SE=1-kp(I)/kp(II).<Umax> is the mean annual wind maximum calculated with kp(II), using Eq. (1).kp (approach II)           12.5-15%          12.5-15%     12.5-17%    15-17%   12.5-16.5%  16.5-17% approach ImaxUMeasurementsIn order to validate the approaches, we use 10 min wind measurements from sixstations in the mid-latitude, see Table II.    We further predict the annual maximumwind using Eq. (1) for the offshore site Horns Rev only while not the land sites in order not to go into discussions aboutthe models’ performance induced by factors other than the model resolution.  Table II, details of measurements ResultsConclusionsThe recipe

Recipe for correcting the effect of mesoscale resolution on the estimation of extreme winds

http://orbit.dtu.dk/ws/files/5500706/Lars%C3%A9n_poster_EWEA2011presentation.pdf

Recipe for correcting the effect of mesoscale resolution on the estimation of extreme winds

Abstract

Similar works

Full text

Available Versions

Online Research Database In Technology