Bumke, Karl

Karger, Uwe

English

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1996 EUROPEAN UNION WIND ENERGY CONFERENCE 20-24 May 1996 GOTEBORG, SWEDEN Spatial Extension of Coastal Impact on the Mean Surface Wind over the Baltic Sea U. Karger and K Bumkelnstitut fiir MeereskundeDiisternbrooker Weg 20, 24105 Kiel, Germany Abstract The coastal wind field over sea is affected by various influences, resulting from the changing terrain. Mean wind wind speedin coastal areas is generally lower than at the open sea. The spatial extension of the coastal impact on the mean wind speedis worked out for the Baltic Sea by a comparison of analysed geostrophic wind with ship observed surface wind. It is shown that the zone of coastal influence on the surface windfield extents up to 50 km distance to the coast with the greatestvariations within the first 20 km. Keywords: Wind speed, Baltic Sea, Resources, Boundary Layer Introduction The coastal zone can be regarded as a transient zone, where the roughness increases from low values at the open sea over higher values in the coastal zone due to influences of limited fetch or water depth on roughness to at least the high roughness of land surfaces. Also the occurance of land or sea breezes affects the surface wind field driven by the large scale pressure field as well as such effects like channeling by the topography of the land. 'Ihus a good knowledge about the coastal impact on the mean surface wind field may be used to give a guide­line for the location of offshore wind power plants. Infor­mation about the coastal influence on the surface wind field is obtained by calculating ageostrophic ratios of observed surface wind to analysed geostrophic wind speed for the 2-year period 1992/1993 as a function of the distance to the coast. Geostrophic winds are estima­ted from pressure observations of voluntary observing ships and coastal weather stations. Validation of estima­ted ageostrophic coefficients was achieved by using inde­pendent data from 1994. Data The dataset to estimate geostrophic wind fields and deri­ve ageostrophic coefficients contains daily observations of ships and synoptic stations at land from January 1992 until December 1993 at 00, 06, 12 and 18 UTC. During this period nearly I 0300 ship wind observations at the Baltic Sea were gathered. Most of the ship observations were located in the south-east parts. There are two kinds of wind observations on voluntary obst-'TVing ships: Direct measurements and Beaufort esti­ma1es 9f wind according to WMO 1100 scale (WMO, 1970). 1be wind speed of direct wind measurements is reduced from an assumed average height of20 m (CAR­OONE, 1990) to I O m using the logarithmic wind profile. Wind estimations on ships should correspond to wind speeds at a height of I Orn (WMO, 1970). Accuracy of ship positions is 0.1 ° latitude and longitude. Validation of surface wind fields was done using the same kind of data from 1994. For 1994 there are about 8300 observations at our disposal. All data were provided by the Deutscher Wetterdienst. The Analysis The analysis scheme was developed at the Institut fur Meereskunde (IfM) in Kiel (ENNENGA, l 985, BUMKE und HASSE, 1989). It is based on the polynomial method (PANOFSKY, 1949; GILCHRIST and CRESSMANN 19 :4 ). ·�e �oly�omial method is applied to each grid pomt of a 1 latitude/ longitude field. A second order pressure field is determined simultaneously by observa­tions of wind and air pressure: x and y are the distances in north and east directions between the positions of observations and grid point. The asterix marks an estimated parameter.The relation of the pressure to the wind field is assumed to be geostrophic. This necessitates a boundary layer parameterisation to get geostrophic winds from surface wind observations. For this purpose a stability dependent approach of LUT­HARDT and HASSE ( 1981) was used, derived for situa­tions with onshore winds in the German Bight. The geostrophic wind components ug· and v/ are given by . aOl + 2 ao2 y + a11 x u, fp (2) . QIO + 2 420 x + a ll y v, fp (3) Here f is the Coriolisparameter and p the air density. The solution of the polynom (I) is given by minimizing the sum S. � is a Cressman-function, which provides for decrasing �ue�ce of an observation with increasing distance to a?11dpomt (CRESSMANN, 1954). W weights the relative influence of pressure and wind observations , for analysis W=0.3 was choosen. Due to the sparsity of ship observations additional infor­�atio� from coastal stations was required. Coastal sta­tlo�s m this context are all synoptic stations with a height of 50 m and less. Due to possible orographic in­fluence wind observations of coastal stations are exclu­ded. Si�ce we intend to derive the spatial extension of coastal influence at sea, only ship wind observations were taken into accmmt, which were not influenced by orographic effects. To fulfill this condition a distance to the coast of more than 100 km was assumed to be suffi­cient. As a consequence all wind observations closer than I OOkm to the coast can be regarded as independent from 564 the analysis. Inteipolation of air temperature and water temperature has been done by averaging the observations linearly over areas of 2° latitude/longitude. Again the information of coastal stations was used for inteipola­tion. Due to the insufficient number of water temperature observations water temperatures have been estimated as a five day average. Comparison to Observations In this chapter analysed fields, inteipolated linearly on the positions of the ship observations, are compared with direct ship observations. The pressure field is well des­cribed by the analysi). This is shown by a correlation coefficient of 0. 99 and an RMS�deviation between calcu­lated and observed air pressure of 1.0 hPa. It should be noted here that the analysis scheme for air pressure observations as well as for wind observations used for analysis includes an error detection scheme. Because of the sparsity of temperature observations erro­neous observations of air or water temperatures cannot be detected. Thus in contrast to the comparison of analy­sed to observed air pressure possibly erraneous tempera­ture data are included in the following. For analysed water temperatures it was found that they are not biased compared to ship measurements. The correlation coeffi­cient is rather high, it is 0.95 For the observed and analysed wind data the following ageostrophic angle were found: The mean difference between analysed and observed wind direction is 26.8°. If the stability is given by the temperature difference between analysed air and sea surface temperature �T.118, the ageostrophic angle a can be described by a linear relation in the range of -3K 2 iff s; 3K. a 24.9 + 1.8 · liT (5) The comparison of analysed geostrophic wind speeds to observations results in a complex correlation coefficient (MARSDEN, 1987) of 0.82, which considers wind direc­tion and wind speed of analysed geostrophic and obser­ved wind vectors. Mean Fields of Geostrophic Wind Speed Fields of geostrophic wind speed and direction have been calculated from synoptic data of the period from 1992 to 1994, every 6 hour. As an examle the result for 1994 is given in Figure 1. The characteristics are that mean wind speed decreases from the south west to the north east. Compared to the conditions at other maritime European regions, e.g. the North Sea and Irish Sea (lROEN and PETER­SEN, 1989), the wind speeds are lower especially in the northern parts of the Baltic Sea. Thus it is very important to make the best use of the potential available wind ener­gy by an optimal siting of the wind turbines. Coastal Influence on the Mean Surface Wind To consider the coastal influence on the mean wind speed a coefficient q is defined to give the ageostrophic ratio of analysed geostrophic and observed mean wind­speeds. This coefficient is calculated for distance clas­ses, because the ship positions are given only in terms of 0.1 °latitude and longitude. 565 Figure 1: Average geostrophic wind speed (m/s] in 1994. 9.6 9.0 e.& e.o 7.0 7.0 6.0 6.0 6.5 o.o 4.6 4.0 3.0 a.o 2.0 a.a l.5 l.O 0.0 0.0 30° Figure 2: Definition of offshore, onshore and minimum distance to coast. The ships position is starting point of the wind vector. Variability of wind direction is given by a. ' i -I .,..,.r . ....-· : i f-oFFSHORE-+--ONSHORE J, I The wind field in coastal areas is influenced by the chan­ge of roughness with higher roughness over land than at sea. If the wind is directed from land to sea, we can ima­gine a zone of wind speed adjustment to the new roughness conditions. Earlier studies (e.g. Theunert, 1986) showed that this zone of adjustment exists on both sides of the shore. So it is to expect that onshore wind speeds will change in a similar manner. Coefficients are calculated for different distance classes to coast. A wind direction dependent distance to coast has been defined in the manner illustrated in Figure 2. Every ship positions onsh?re '_'Ild offshore coast distances have been assigned cons1denng the wind direction. The v�ability of wind direction is taken into account by averagmg the coast distance for an interval of analysed wind direction ±20°, taking the ageostrophic angle accor­ding to equation (5) into account. The variability of wind d�ection is assumed to be independent of wind speed. Smee onshore as well as offshore winds have a zone of adjustment to new roughness conditions, it can be expec­ted that the distribution of land and sea will have some impact on the surface wind field. Thus the ageostrophic ratio should be a function of a combination of onshore and offshore distances(Tab. 1) to the coast, giving smal­lest coefficients q in semi-enclosed areas with a small onshore and a small offshore distance to the coast. Table 1: The ratio q of observed surface to analysed geostrophic windspeed for classes of onshore (rows) and offshore (columns) distances to the coastline. q offshore distance [km) -5 -10 -20 -30 -50 >50 0 -5 .48 .45 .65 .56 .63 .78 n s -JO .55 .59 .70 .77 .77 .68 h 0 .72 .73 .80 .74 r -20 .62 .63 e -30 .65 .74 .73 .72 .79 .76 d i -50 .76 .75 .81 .79 .75 .83 s >50 .74 .79 .78 .85 .83 .86 Coastal impact on wind speed reaches up to 50 km from the coastline, while for distances greater than 50 km the coefficient q show no significant variations. In the di­stance classes up to 20 km the ratio between analysed and observed surface wind speed shows the greatest va­riations. from 20 to 50 km offshore and onshore distance to the coast the value of q increases only from 0.72 to 0.86 . This means that a significant impact on the mean windfield will vanish after 20 km offshore distance to the coast. For winds blowing along the coast there are no onshore and offshore distances to the coast defined, in this case the coefficients q are calculated as a function of the mi­nimum distance to the coast (Fig. 2). '!be results arc gi­ven in Table 2. Table 2: 'Ibe ratio q of observed surface to analysed geostrophic wind speed for classes of minimum distances to the shore. dist. !km] -5 -1 () -15 -20 >20q .63 .76 .80 .83 .86 Mean Fields of Surface Winds Tables l and 2 can be used to estimate factors of wind speed reduction for each coastal area as a function of mean relative wind direction. If a large extension of open sea (in this case the distance to the opposite coast is grea­ter than 50 km) is adjascent at a coastal site, slow chan­ges of the coefficients q are evident. In the onshore case the range of q is between 0.74 and 0. 86 and for offshore case q varies from 0. 68 to 0.86. In semi-enclosed areas that means onshore and offshore distances are smaller 20 km. the coefficient q has values between 0.48 and 0.72 . Generally coastal inf1uencc on the mean surface wind field will have an impact during the first 50 km distance to the coast line. lbc changes of wind speed at the land-sea transition appears to be composed of two parts: the more pronounced change within 15 to 20 km next to shore due to the different roughnesscs of land and sea and a change due to a variation in roughness due to th� sea state _in co�st�l waters, e. g. induced by change ofdepth or Jetch hm1tat10ns. Applying the coefficients of Table 1 and 2 on the geostrophic wind fields, we get as expected a remarkably high reduction of the mean wind speed in coastal areas. This is shown in an example for the year 1994 in Figure 3. Figure 3: Surface wind speed [m/s] in 1994, calculated from geostrophic winds by applying the ageostrophic coefficients of Tables 1 and 2 . 566 9.5 9.0 8.5 e.o 7.6 7.0 G.6 B.0 5.5 ,.o 4.5 4.0 35 3.0 2.S 2.0 1.6 1.0 05 0.0 Generally wind speeds are highest during winter months and lowest during summer months. So we get the follo­wing picture: In areas with coast lines orientated parallel to the mean wind direction the coastal impact on the mean wind speed is small and limited to distances to the coast of less than about 1 O km. In semi-enclosed areas and at coasts orientated perpendicular to the mean wind direction the coastal impact on the mean wind �'Peed is high and should not be neglected up to down- or upwind distances of about 20 km to the coast. Because ageostro­phics ratios of surface wind speed to gcostrophic wind speed are calculated as a function of onshore and offs­hore distances to the coast only, all other processes, which may inf1uence the surface wind field are included in a statistical sense only. The coefficient 'q can depend on prametcrs like the windspced and the stability over the coastal near sea surface. which is not considered in this approach. Validation Validati?n of estimated ageostrophic coefficients has been achieved by using analysed geostrophic wind fields and ship wind observations from 1994. lbcse arc inde­pendent from the calculated ageostrophic coefficients because these have been estimated from data of 1992 and l993only. For the year 1994 the following relationships between analysed and observed wind are found. The complex c�rrelation coefficient is 0.83 for observed and analysed wmd. The annual analysed mean wind speed has no bias compa�cd to ship observations of wind speed; the mean v�lue �or all ship observations and for analysed surface ':"mds I� 7.1 m/s. The variability of the wind speed field Is dcscnbed well by the interpolated wind field; the stan­dard d.eviation of analysis is 4.2 m/s compared to 4.0 m/s r�s�ltmg from ship observations of wind speed. 'Ibe va-1:ation �f wind speed is depicted by the Weibull distribu­tions of wmd speed p(U). 'Ibe function is determined by l I I I I I I II the two fonn parameters A und k. p(U) !.. (� )1t-1 exp(-(� )" )A A A (6) Figure 4 show the Weibull functions fitted to the obser­vations and the analysis. The agreement for the IfM sur­face wind analysis using the ageostrophic coefficients to the observations is good, while the Weibull function estimated for surface wind speeds of the European Area Model of the Gennan Weather Service does not fit the Weibull distributions of the direct observations well. Figure 4: Weibull distribution of surface wind speeds for the total Baltic Sea: full line: ship observations;dotted line: European Area Model; dashed dotted: this study 0.14�--------·-�-�------� 0.12 0.1 0.08 S 10 IS 20 2S - •i-<l U (mls) High wind speeds over sea are underestimated and mode­rate wind speeds are overestimated by the European Area Model. 1bis is due to an underestimation of the mean value and the variance of wind speed. In this approach ageostrophic coefficients depend on coastal distances only. Thus e.g. stability effects are neglected in the boundary layer. It is to expect that the­refore seasonal means are not represented as well as the annual mean due to an annual cycle of stability. We conclude that this analysis method is suitable to cal­culate annual mean wind speeds and the annual variance of the wind speed over the Baltic sea. For seasonal in­vestigations a greater data base is neccessary·to take the dependence on coastal distances and stability into ac­count. A first approach by dividing the whole data set into three categories; stabil, neutral, and instabil due to air-sea temperature differences, results in a better estima­te of the monthly mean, but the uncertainties in the esti­mated ageostrophic coefficients of each distance int�rvalare high due to the not sufficient number of observations. Another result of this study is that the coastal influence on the mean wind speed is only marginal at distances of more than about 15 to 20 km to the coast. 'Ibis should be reflected in measurements of drag coefficient, too. Measurements of drag coefficients on our R.V. ALKOR using dissipation method showed (NEUGUM, 1995) that within a 10 km distance to the coast of Lolland drag co­efficient is given by CDN " ( 0.87 + 0.0673 'U10 ) • 10·3 567 corresponding for 7 m/s wind speed to CDN = 1.34 . 10-3 •At the open sea east of Gotland drag coefficient is esti­mated to CDN = ( 1.11 ± 0.17 ) . 10·3 for wind speeds ranging from about 5 to 12 m/s. Thus measurements of drag coefficient correspond well to the coefficients estimated by this statistical approach of ageostrophic coefficients as a function of the distance to the coast for the Baltic Sea. Acknowledgments The synoptic data of the years I 992 to 1994 were kind! y provided by the Deutscher W etterdienst. References [I] BUMKE, K. and L. HASSE, 1989 : An AnalysisScheme for Determination of True Surface Winds at Seafrom Ship Synoptic Wind and Pressure Observations,Boundary Layer Meteorology, 47, 295-308[2] CARDONE, V.J., J.G. GREENWOOD und M.A.CANE, 1990 : Trends in Historical Wind Data, Journalof Climate, 3, 113-127[3] ENNENGA, U., 1985: Objektive Analyse aktucllcrWind-und Druckfelder Uber dem Nordatlantik, Bcrichteaus dem Institut fur Meereskunde Kiel, I 42, l 03 p.[4] GILCHRIST, B. and G.P. CRESSMANN, 1954 : AnExperiment in O�jective Analysis System, Tellus, 6,309-318f5] LITTRARDT, H. and L. HASSE. 1981 : On the Rela­tionship Between Surface and Geostrophic Wind in the Region of the Gennan Bight, Contributions to Atmosphe­ric Physics, 54, 222-237 [6] MARSDEN, R.F., 1987 : A Comparison betweenGeostrophic and Directly Measured Surface Winds overthe Northeast Pacific Ocean, Atmosphere-Ocean, 25(4),387-401[7] NEUGUM, A., 1995: Systematische EinOiissc auf dieBestimmung der Schubspannung mit dcr "Dissipations­methode" aufSee, Dissertation, Institut flir MeereskundeKiel, 107 p.[8] PANOFSKY, H.A., 1949: Objective Weather MapAnalysis, Journal of Meteorology, 6, 386-392[9] THEUNERT, S., 1986 : Anwendung eines Mesoska­len-Modells zur Bestimmung des natiirlichenWindenergie-angebotes im deutschen Kiistenbereich.Beriehte des Instituts fiir Meteorologie und Klimatologieder Universitat Hannover, 27, 138 p.[ l OJ lROEN, I.,PETERSEN, E.L., 1989 : EuropeanWindatlas, Risoe National Laboratory, Roskilde, Den­mark, 656 p.fI I] WORLD MiffEOROLOGICAL ORGANISATION,1970: Marine Science Affairs. Report Nr. 3 : The Beauf'­ort Scale of Wind Force;WMO, C',en( Schwciz, 22 p.

Spatial extension of coastal impact on the mean surface wind over the Baltic Sea

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Spatial extension of coastal impact on the mean surface wind over the Baltic Sea

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