O objetivo principal deste trabalho é avaliar a capacidade do modelo RegCM3 (Regional Climate Model versão 3), para três diferentes simulações, em simular os padrões espaciais de tendência de alguns índices climáticos anuais e sazonais de temperatura e chuva na América do Sul para o clima presente (1961-1990). Para atingir o objetivo principal, desenvolveu-se um algoritmo baseado no software RClimDex para calcular os índices e investigou-se a habilidade dos dados de reanálise do NCEP/NCAR e do conjunto de dados de chuva produzido por Liebmann e Allured (2005) em estimar os índices anuais e sazonais de temperatura e chuva para a mesma região e período. A metodologia concentrou-se em análises dos coeficientes de correlação e de regressão linear. De maneira geral, os dois conjuntos de dados foram considerados válidos para representar os índices anuais e sazonais de temperatura e chuva observados na América do Sul durante o período de 1961-1990. Contudo, a reanálise do NCEP/NCAR mostrou sinais de tendência opostos às observações para os índices de temperatura e chuva sobre a Argentina. Dentre as três simulações com o modelo RegCM3, as simulações RegCM3(s2), com o esquema de convecção Emanuel, e RegCM3(s1), com o esquema de Grell, apresentaram um melhor desempenho em representar as tendências dos índices de temperatura e chuva, respectivamente, sobre a América do Sul. O modelo RegCM3(s1) simulou a predominante tendência de aumento das condições de umidade observadas na América do Sul através dos índices anuais r95p, prcptot e rx5day, particularmente na estação de inverno. As tendências opostas dos índices anuais cwd e cdd, que sugerem um aumento no número de dias com chuva, ao contrário, não foram bem detectadas pelo modelo RegCM3(s1). O aquecimento da temperatura mínima como conseqüência do aumento de noites quentes e diminuição de noites frias identificado em quase todo o continente foi simulado corretamente pelo modelo RegCM3(s2). Com relação à temperatura máxima, embora as observações não apresentem um padrão característico, a simulação do modelo RegCM3(s2) se mostra bastante semelhante ao apresentado pela reanálise do NCEP/NCAR. As distribuições espaciais de tendência dos índices anuais de temperatura e chuva resultantes das simulações do RegCM3(s2 e s1, respectivamente) e do HadAM3 sobre a América do Sul durante o período de 1961-1990 são bastante semelhantes entre si, embora o HadAM3 seja caracterizado por tendências mais suavizadas. Para os índices anuais e sazonais de temperatura, a simulação do HadAM3 se mostrou ligeiramente melhor à do RegCM3, enquanto que o inverso é encontrado para os índices anuais e sazonais de chuva. / The main goal of this study is to investigate if the RegCM3 model (Regional Climate Model version 3) is able to simulate the spatial patterns of some annual and seasonal climate indices of temperature and precipitation trend over South America for the present climate (1961-1990). The analysis is done for three different simulations where the model was running with different cumulus parametrization, i.e, Grell and Emanuel. An algorithm based on the software RClimDex was developed to calculate the indices. Different data sources such as the NCEP/NCAR reanalysis, individual observational stations and others were used to estimate the annual and seasonal indices of temperature and precipitation for the same region and period. Correlation and linear regression coefficients analysis were used in the results comparison. In general, the results suggest that the datasets can provide useful information about annual and seasonal indices of temperature and precipitation at individual grid cells in South America during the period 1961-1990. However, the NCEP/NCAR reanalysis showed reversal trend signals for some indices over Argentina. Within the three simulations with the RegCM3 model, the trends of the annual and seasonal indices of temperature and precipitation over South America are better reproduced by the Emanuel (s2) and Grell (s1) schemes, respectively. The RegCM3(s1) simulates the change to wetter conditions in South America through the r95p, prcptot and rx5day annual indices, particularly in the austral winter. On the other hand, the opposite signal of the trends in cdd and cwd indices, that indicates an increase in the number of days with precipitation, was not well represented by the model. The warming in minimum temperature as a consequence of the increase in the frequency of warm nights and the decrease of cold nights observed all over the South American continent was correctly simulated by the RegCM3(s2). Although the observed maximum temperature extremes did not show any special feature, the simulations with Grell scheme were able to represent the spatial patterns of the warm and cold days indices trend similar to the NCEP/NCAR reanalysis. The spatial distributions of the annual indices of temperature and precipitation trend obtained from the RegCM3(s2 and s1, respectively) and HadAM3 simulations over South America during the period 1961-1990 are very similar, though the HadAM3 shows a trend less intense. For the annual and seasonal indices of temperature, the HadAM3 simulation is slightly better than the RegCM3 running, while the opposite is found to the annual and seasonal indices of precipitation.

O objetivo principal deste trabalho é avaliar a capacidade do modelo RegCM3 (Regional Climate Model versão 3), para três diferentes simulações, em simular os padrões espaciais de tendência de alguns índices climáticos anuais e sazonais de temperatura e chuva na América do Sul para o clima presente (1961-1990). Para atingir o objetivo principal, desenvolveu-se um algoritmo baseado no software RClimDex para calcular os índices e investigou-se a habilidade dos dados de reanálise do NCEP/NCAR e do conjunto de dados de chuva produzido por Liebmann e Allured (2005) em estimar os índices anuais e sazonais de temperatura e chuva para a mesma região e período. A metodologia concentrou-se em análises dos coeficientes de correlação e de regressão linear. De maneira geral, os dois conjuntos de dados foram considerados válidos para representar os índices anuais e sazonais de temperatura e chuva observados na América do Sul durante o período de 1961-1990. Contudo, a reanálise do NCEP/NCAR mostrou sinais de tendência opostos às observações para os índices de temperatura e chuva sobre a Argentina. Dentre as três simulações com o modelo RegCM3, as simulações RegCM3(s2), com o esquema de convecção Emanuel, e RegCM3(s1), com o esquema de Grell, apresentaram um melhor desempenho em representar as tendências dos índices de temperatura e chuva, respectivamente, sobre a América do Sul. O modelo RegCM3(s1) simulou a predominante tendência de aumento das condições de umidade observadas na América do Sul através dos índices anuais r95p, prcptot e rx5day, particularmente na estação de inverno. As tendências opostas dos índices anuais cwd e cdd, que sugerem um aumento no número de dias com chuva, ao contrário, não foram bem detectadas pelo modelo RegCM3(s1). O aquecimento da temperatura mínima como conseqüência do aumento de noites quentes e diminuição de noites frias identificado em quase todo o continente foi simulado corretamente pelo modelo RegCM3(s2). Com relação à temperatura máxima, embora as observações não apresentem um padrão característico, a simulação do modelo RegCM3(s2) se mostra bastante semelhante ao apresentado pela reanálise do NCEP/NCAR. As distribuições espaciais de tendência dos índices anuais de temperatura e chuva resultantes das simulações do RegCM3(s2 e s1, respectivamente) e do HadAM3 sobre a América do Sul durante o período de 1961-1990 são bastante semelhantes entre si, embora o HadAM3 seja caracterizado por tendências mais suavizadas. Para os índices anuais e sazonais de temperatura, a simulação do HadAM3 se mostrou ligeiramente melhor à do RegCM3, enquanto que o inverso é encontrado para os índices anuais e sazonais de chuva. / The main goal of this study is to investigate if the RegCM3 model (Regional Climate Model version 3) is able to simulate the spatial patterns of some annual and seasonal climate indices of temperature and precipitation trend over South America for the present climate (1961-1990). The analysis is done for three different simulations where the model was running with different cumulus parametrization, i.e, Grell and Emanuel. An algorithm based on the software RClimDex was developed to calculate the indices. Different data sources such as the NCEP/NCAR reanalysis, individual observational stations and others were used to estimate the annual and seasonal indices of temperature and precipitation for the same region and period. Correlation and linear regression coefficients analysis were used in the results comparison. In general, the results suggest that the datasets can provide useful information about annual and seasonal indices of temperature and precipitation at individual grid cells in South America during the period 1961-1990. However, the NCEP/NCAR reanalysis showed reversal trend signals for some indices over Argentina. Within the three simulations with the RegCM3 model, the trends of the annual and seasonal indices of temperature and precipitation over South America are better reproduced by the Emanuel (s2) and Grell (s1) schemes, respectively. The RegCM3(s1) simulates the change to wetter conditions in South America through the r95p, prcptot and rx5day annual indices, particularly in the austral winter. On the other hand, the opposite signal of the trends in cdd and cwd indices, that indicates an increase in the number of days with precipitation, was not well represented by the model. The warming in minimum temperature as a consequence of the increase in the frequency of warm nights and the decrease of cold nights observed all over the South American continent was correctly simulated by the RegCM3(s2). Although the observed maximum temperature extremes did not show any special feature, the simulations with Grell scheme were able to represent the spatial patterns of the warm and cold days indices trend similar to the NCEP/NCAR reanalysis. The spatial distributions of the annual indices of temperature and precipitation trend obtained from the RegCM3(s2 and s1, respectively) and HadAM3 simulations over South America during the period 1961-1990 are very similar, though the HadAM3 shows a trend less intense. For the annual and seasonal indices of temperature, the HadAM3 simulation is slightly better than the RegCM3 running, while the opposite is found to the annual and seasonal indices of precipitation.

The profitability from the production wind power energy is related to the quality of the wind speed forecasts. All wind predicting methods needs meteorological data, for the prevailing synoptic situation, as input. High quality input is wanted for a better result. In this study a new idea of a method for estimation of high resolution wind fields is examined. The idea is to use an existing database, containing simulations of high resolution wind fields, to estimate the actual wind by combining the simulations in a way fitting actual synoptic data. The simulations in the database have been produced by the mesoscale MIUU-model, which has been developed by Leif Enger at Uppsala University. The database contains simulations characterized by different geostrophic wind speeds and directions. There is also a separation into four seasons, where values which are typical for each season is put on meteorological parameters. Reanalysis data from NCEP-NCAR, containing 850 hPa geopotential heights describing actual synoptic situations, is used to calculate geostrophic wind speeds and directions. Three different geostrophic wind calculation methods, the triangle method, the small cross-method and the large cross-method, are tested. The calculated geostrophic wind is compared between the methods. The small cross-method is chosen and the main reason for that is the large amount of reanalysis information considered by this method and the use of a small calculation area. Measurements of the wind speed and direction are available from the tower at Utgrunden. The geostrophic wind speeds and directions are therefore calculated especially for the position of Utgrunden. This is done by a linear weighting of data, from several grid points close to Utgrunden, with respect to the distance to Utgrunden. Linear weighting is also used when estimating the wind speed for Utgrunden. The wind speed is estimated by weighting together MIUU-model simulations, for different geostrophic wind speeds and directions, so that they fit the geostrophic wind values calculated for Utgrunden. The calculated wind speed, measured wind speed and calculated geostrophic wind speed, for Utgrunden, are compared. The correspondence, between the calculated and measured wind speed, turns out to be quite good for many time periods. The diurnal variations in the measured wind speed are partly captured by calculated wind speed, but the diurnal variations tend to be larger in the measured wind speed then in the calculated. There are also cases where there are large differences between the measured and estimated wind speed. Many of these cases are probably cased by unusual weather situations. By considering additional parameters, as the temperature field, it is likely that these wind estimations can be improved. With more research it may be possible to produce high resolution wind fields with enough accuracy to be useful as inputs in wind prognostic systems. The advantage with such a method would be that accurate high resolution wind fields could be produced without the use of a time consuming numerical high resolution model. / Lönsamheten för produktion av vindkraft elektricitet bestäms delvis av förmågan att göra bra vindprognoser för nästkommande dygn. Alla metoder för vindprognostisering behöver meteorologisk indata som beskriver den rådande synoptiska situationen. Kvaliteten och upplösningen på dessa indata har stor betydelse för metodens resultat. I denna studie undersöks en alternativ metod för bestämning av högupplösta vind fält. Idén är att man ska försöka utnyttja en tillgänglig databas av högupplösta vindfält, producerade av den mesoskaliga MIUU – modellen som är utvecklad av Leif Enger på meteorologiska institutionen vid Uppsala Universitet. Tanken är att dessa vindfält ska kunna kombineras på ett sådant sätt att de överensstämmer med en given synoptisk situation. MIUU – modell körningarna, i databasen, är indelade i situationer karaktäriserade av olika värden på den geostrofiska vindstyrkan och vindriktningen. Körningarna är gjorda för fyra säsonger, för vilka typiska värden för säsongen är satta på styrande parametrar. För att kunna kombinera MIUU - modell körningarna beräknas den geostrofiska vinden från 850 hPa geopotential höjd återanalys data tillgänglig från NCEP-NCAR. Tre olika beräkningsmetoder för geostrofisk vind testas och jämförs. Den ”lilla korsmetoden” väljs för uppgiften beroende på att den utnyttjar en förhållandevis stor mängd återanalys data, för beräkning av geostrofisk vind, samt använder litet beräkningsområde. Automatiskt uppmätta värden över vindhastighet och vindriktning finns tillgängliga från en mast positionerad vid Utgrunden i Kalmar sund. Den geostrofiska vinden beräknas därför i Utgrundens position. Beräkningen utförs genom linjär viktning av data från de från Utgrunden sett fem närmaste gridpunkterna (i lilla korsmetodens gridfält). En linjär viktning används sedan även för att vikta ihop de MIUU – modell simulerade vindfälten så att de passar de beräknade värdena på geostrofisk vindhastighet och vindriktning. Jämförelser görs mellan den beräknade vinden, den uppmätta vinden samt den geostrofiska vinden, för Utgrunden. Korrelationen, mellan uppmätt och beräknad vind, visar sig vara ganska god periodvis. Den dagliga variationen i den uppmätta vindhastigheten fångas delvis av beräkningsmetoden, men dygnsvariationen är betydligt större i den uppmätta vinden än i den beräknade. Det noteras även att det finns situationer då det är stora skillnader mellan beräknad och uppmätt vind. Dessa situationer beror i många fall troligen på onormala vädersituationer. Studium av ytterliggare parametrar, som t.ex. temperaturfältet, skulle troligen leda till betydande förbättringar i vinduppskattningen. Ytterligare forskning och förbättring av metoden skulle kunna leda till produktion av högupplösta vindfält med tillräcklig kvalitet för användning i vindprognostiseringsmodeller. Fördelen skulle i så fall vara möjligheten att kunna producera högupplösta vindfält utan användning av tidskrävande numerisk modeller.

Boundaries between air masses, called frontal zones, have been associated with vegetation boundaries (Bryson, 1966; Hare and Ritchie, 1972). Using gridded climate reanalysis data, we analyze the air masses and frontal zones of North America. The position of the July Arctic front varies significantly through the period 1948-2007, with a mean position similar to that found by Bryson (1966). The variability of the front position can be associated with changes in the general circulation; when the AO and SOI are positive (negative), the position of the July Arctic front is further north (south). There is also more variability in the July Arctic front position in Eastern versus Western Canada. When the July Arctic front is north (south) of the mean position, the boreal forest and tundra vegetation is more (less) productive. There is some evidence that climate warming is only starting to shift the July Arctic front to the north. / This study was funded by the Natural Sciences and Engineering Research Council (NSERC) and the Polar Climate Stability Network (PCSN) project funded by the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS).

Boundaries between air masses, called frontal zones, have been associated with vegetation boundaries (Bryson, 1966; Hare and Ritchie, 1972). Using gridded climate reanalysis data, we analyze the air masses and frontal zones of North America. The position of the July Arctic front varies significantly through the period 1948-2007, with a mean position similar to that found by Bryson (1966). The variability of the front position can be associated with changes in the general circulation; when the AO and SOI are positive (negative), the position of the July Arctic front is further north (south). There is also more variability in the July Arctic front position in Eastern versus Western Canada. When the July Arctic front is north (south) of the mean position, the boreal forest and tundra vegetation is more (less) productive. There is some evidence that climate warming is only starting to shift the July Arctic front to the north. / This study was funded by the Natural Sciences and Engineering Research Council (NSERC) and the Polar Climate Stability Network (PCSN) project funded by the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS).

Boundaries between air masses, called frontal zones, have been associated with vegetation boundaries (Bryson, 1966; Hare and Ritchie, 1972). Using gridded climate reanalysis data, we analyze the air masses and frontal zones of North America. The position of the July Arctic front varies significantly through the period 1948-2007, with a mean position similar to that found by Bryson (1966). The variability of the front position can be associated with changes in the general circulation; when the AO and SOI are positive (negative), the position of the July Arctic front is further north (south). There is also more variability in the July Arctic front position in Eastern versus Western Canada. When the July Arctic front is north (south) of the mean position, the boreal forest and tundra vegetation is more (less) productive. There is some evidence that climate warming is only starting to shift the July Arctic front to the north. / This study was funded by the Natural Sciences and Engineering Research Council (NSERC) and the Polar Climate Stability Network (PCSN) project funded by the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS).

Boundaries between air masses, called frontal zones, have been associated with vegetation boundaries (Bryson, 1966; Hare and Ritchie, 1972). Using gridded climate reanalysis data, we analyze the air masses and frontal zones of North America. The position of the July Arctic front varies significantly through the period 1948-2007, with a mean position similar to that found by Bryson (1966). The variability of the front position can be associated with changes in the general circulation; when the AO and SOI are positive (negative), the position of the July Arctic front is further north (south). There is also more variability in the July Arctic front position in Eastern versus Western Canada. When the July Arctic front is north (south) of the mean position, the boreal forest and tundra vegetation is more (less) productive. There is some evidence that climate warming is only starting to shift the July Arctic front to the north. / This study was funded by the Natural Sciences and Engineering Research Council (NSERC) and the Polar Climate Stability Network (PCSN) project funded by the Canadian Foundation for Climate and Atmospheric Sciences (CFCAS).

The expected technical lifetime for a wind turbine is 20-25 years (Wizelius, 2007). In the process of planning a wind farm on a site an estimation of the average wind speed and the energy yield is required from the site. Due to large fluctuations in wind velocity from one year to another it is, from a climatologically point of view, not sufficient to measure the wind for a short period of time, e.g. one year. The year measured may have uncommonly high or uncommonly low winds and thereby generate an average not representative of the wind climate on the site. In the same time it is neither practical nor economically desirable to measure for a longer time period. Instead the measured data collected for a short period of time is scaled into a normal year before it is used to calculate the energy content. This normal year correction can be preformed using different methods. Principally, the methods relate the short time series, measured on the site, to one or more variables of a long time reference series. As a long time reference series the geostrophic wind on the site or a series of measured wind nearby can be used. If the correlation between the two series is sufficiently high a normal year correction may be done using the relation. Normal year correction has foremost been done using the relation between the velocities of the measured wind and a reference wind. The purpose in this study is to evaluate and implement a part of an algorithm for normal year correction considering additional variables besides wind velocity. The relationships studied are between measured wind speed and geostrophic wind speed, geostrophic wind direction and time of the year. For the purpose of evaluating the algorithm two wind data series from Näsudden, on the Swedish island of Gotland, for a period of 15 years has been used. Measured wind on 75 meters and geostrophic wind on 850hPa. Where the geostrophic wind has been used as a long time reference and the measured wind for one year at the time has been related to this reference. The relation has then been used together with the geostrophic wind data to create an estimation of the wind climate on Näsudden in three steps. Step one establishes the estimation due to the relation of geostrophic wind speed. Step two corrects the estimation due to the relation of geostrophic wind direction and step three corrects the estimation due to the relation of time of year. The conclusions from this study showed that with the method used for implementing the algorithm the normal year correction using only the relation between the velocities of the measured wind and the geostrophic wind gives the best estimation of the climatically mean wind speed. The standard deviation gives a 5 % risk for more than 0.436 m/s error for estimation of the mean wind on the site, which is comparable to earlier studies. The introduction of the wind direction dependence overestimates the mean wind on the site and amplifies the error. The introduction of the season dependence amplifies the error further and overestimates the mean wind additionally. / En vindturbin har en planerad livslängd på 20-25 år (Wizelius, 2007). Vid planering av en vindkraftspark behöver en estimering av medelvinden och energiutvinningen utföras för platsen man är intresserad av. På grund av stora fluktuationer av vindhastighet från år till år är det ur klimatologisk synpunkt inte tillräckligt att mäta under en kort period, exempelvis ett år. Det år man mäter kan ha ovanligt starka eller ovanligt svaga vindar och ge en icke representativ bild av vindklimatet på platsen. Samtidigt är det inte praktiskt eller ekonomiskt önskvärt att mäta under en längre tidsperiod. Istället kan den korta mätserie som insamlats korrigeras till ett normalår med hjälp av en långtidsreferens innan den används för att beräkna energiinnehållet. I princip går normalårskorrigering ut på att relatera den korta mätserien till en eller flera variabler i långtidsreferensen. Som långtidsreferensdata kan den geostrofiska vinden på platsen eller en lång mätserie från en närliggande plats användas. Om korrelationen mellan de två serierna är tillräckligt hög kan en normalårskorrigering göras med hjälp av relationen. Tidigare har man vid normalårskorrigering främst sett till relationen mellan vindhastigheterna för den uppmätta vinden och en referensvind. Syftet i detta arbete är att utvärdera en del av en algoritm för normalårskorrigering där hänsyn tas till fler variabler än endast vindhastighet. Samband som studeras är mellan uppmätt vindhastighet och geostrofisk vindhastighet, geostrofisk vindriktning och tid på året. För utvärderingen av algoritmen har två vinddataserier från Näsudden på Gotland använts för en period av 15 år med uppmätt vind på 75m och geostrofisk vind på 850hPa. Där den geostrofiska vinden fått representera långtidsreferensen och den uppmätta vinden för ett år i taget har relaterats till denna. Efter normalårskorrigeringen har den uppmätta vinden för 15 år fått representera vindklimatet på platsen som jämförelse. Enligt algoritmen har uppskattning av vindklimatet på Näsudden skapats i tre steg. Steg 1 är en uppskattning av vinden från sambandet för geostrofisk vindhastighet. Steg 2 är en korrektion av uppskattningen genom sambandet till geostrofisk vindriktning och steg 3 en korrektion av uppskattningen genom sambandet till tid på året. Efter vart steg skickas det aktuella estimatet vidare till nästa steg där det korrigeras med avseende på nästa samband. Slutsatserna från undersökningen visade att med den metod som använts ger normalårskorrigeringen med enbart sambandet till hastighet bäst uppskattning av den klimatologiska medelvinden. Standardavvikelsen för estimatet ger 5 % risk för mer än 0,436 m/s fel vid uppskattning av klimatologisk medelvind vilket är jämförbart med tidigare studier. Vidare överskattar införandet av vindriktningsberoendet den uppskattade medelvindhastigheten samt ökar osäkerheten. Även införandet av säsongsberoendet överskattar medelvindhastigheten ytterligare samt även osäkerheten.

<p>The procedure of determining if a site is suitable for wind power production requiresconvincing statistical data describing the long-term behavior of the average wind speed.This can be achieved by measuring the wind speed for a short time period, e.g. a year,and after that a Measure-Correlate-Predict (MCP) method can be performed. The shorttermmeasured wind data must be used in combination with a long-term referenceseries. This long-term reference series can be global reanalysis data reaching 20 to 30years back in time. In a MCP method different regression methods can be used. Aftercreating a long-term corrected wind data series, it is possible to analyze the conditionsat the investigated site. To be able to study the behavior of different reference series andregression methods, a model was created in MATLAB. As short-term wind speed dataVattenfall Wind Power supplied data from two measuring masts, Ringhals andOskarshamn, with maximum heights of 96 and 100 meters, respectively. From UppsalaUniversity data were supplied from a measuring mast near Marsta with maximummeasurement height of 29 meters.When creating these long-term corrected wind data series there were many methodsavailable. In this Master thesis methods such as Ordinary-Least-Square, Least-Absolute-Deviation and Reduced-Major-Axis regression methods have been used. With eachmethod three reference series were used in combination with the short-termmeasurement data. These were data from NCAR 850 hPa, NCAR 42-meter sigma leveland a confidential source.Regression methods in combination with reference series were studied and the deviationfrom mean wind speed was obtained for each of these cases. Studies were performed onhow the length of the short-term measurement series affected the deviation from themeasured mean wind speed. It was also investigated if the time of the year had anyinfluence on the measurements.The general conclusion drawn after performing the above-mentioned studies was thatthe NCAR 850 hPa wind speed data and the Reduced-Major-Axis regression methodgave the smallest deviation from the measured mean wind speed in most cases. It wasalso concluded that when a short-term measurement series reached 10 to 14 monthsthere was a significant decrease in deviation from the mean wind speed, regardless ofreference series or method used. Calculations from the model regarding seasonaldependence stated that there was a slight dependency on which period of the year ameasurement was performed.</p> / <p>I processen att bedöma om en plats är lämplig för utbyggnad av vindkraft måste detfinnas övertygande statistiska data som beskriver den genomsnittliga vindhastighetenöver en längre tid. Genom att utföra vindhastighetsmätningar på den tänkta platsenunder en kortare tid, exempelvis ett år, och därefter tillämpas en Measure-Correlate-Predict (MCP) metod i kombination med en långtidsreferens, exempelvis en globalmodell som sträcker sig 20 till 30 år bakåt i tiden kan detta göras. I en MCP-metod kanolika typer av regressionsmetoder användas. När en långtidskorrigerad vinddataseriefinns tillgänglig kan dess beteende på den tänkta platsen analyseras. För att kunna göradetta för flera olika typer av referensserier och regressionsmetoder skapades en modell iMATLAB. Två vinddataserier erhölls från Vattenfall Vindkraft. Dessa var Ringhals ochOskarshamn med högsta mäthöjd på 96 respektive 100 meter. En ytterligarevinddataserie erhölls av Uppsala Universitet från en mätmast nära Marsta med högstamäthöjd på 29 meter.Det fanns flera metoder tillgängliga för att skapa de långtidskorrigeradevinddataserierna. I det här examensarbetet har metoderna Ordinary-Least-Square-,Least-Absolute-Deviation- och Reduced-Major-Axis regressioner använts. För varjemetod testades tre referensserier i kombination med de kortare vinddataserierna. Dessavar NCAR 850 hPa vindhastigheter, NCAR 42 meters sigmanivå vindhastigheter ochannan meteorologisk data.Regressionsmetoderna utvärderades genom att avvikelsen från de kortare mätseriernasmedelvindhastigheter beräknades. Det undersöktes också hur längden på användvinddata från de kortare mätserierna påverkade avvikelsen i medelvindhastighet och omdet fanns något säsongsberoende på när under året som mätningen av vinddata vargjord.Slutsatserna från undersökningarna var att NCAR 850 hPa vindhastigheter ochregressionsmetoden Reduced-Major-Axis generellt gav de lägsta avvikelserna frånuppmätt medelvindhastighet. Slutsatser kunde också dras om längden av användmätdata. Det var tydligt att oavsett referensserie och regressionsmetod uppstod enminskningen i avvikelse från medelvindhastigheten mellan 10 till 14 månaders längd påmätserien. Resultat angående säsongsberoende kunde påvisas i form av avvikelsermellan mätningar gjorda under olika tidpunkter på året. Storlek och tecken påavvikelsen berodde på vilken referensserien i kombination med regressionsmetod somanvändes.</p>

The procedure of determining if a site is suitable for wind power production requiresconvincing statistical data describing the long-term behavior of the average wind speed.This can be achieved by measuring the wind speed for a short time period, e.g. a year,and after that a Measure-Correlate-Predict (MCP) method can be performed. The shorttermmeasured wind data must be used in combination with a long-term referenceseries. This long-term reference series can be global reanalysis data reaching 20 to 30years back in time. In a MCP method different regression methods can be used. Aftercreating a long-term corrected wind data series, it is possible to analyze the conditionsat the investigated site. To be able to study the behavior of different reference series andregression methods, a model was created in MATLAB. As short-term wind speed dataVattenfall Wind Power supplied data from two measuring masts, Ringhals andOskarshamn, with maximum heights of 96 and 100 meters, respectively. From UppsalaUniversity data were supplied from a measuring mast near Marsta with maximummeasurement height of 29 meters.When creating these long-term corrected wind data series there were many methodsavailable. In this Master thesis methods such as Ordinary-Least-Square, Least-Absolute-Deviation and Reduced-Major-Axis regression methods have been used. With eachmethod three reference series were used in combination with the short-termmeasurement data. These were data from NCAR 850 hPa, NCAR 42-meter sigma leveland a confidential source.Regression methods in combination with reference series were studied and the deviationfrom mean wind speed was obtained for each of these cases. Studies were performed onhow the length of the short-term measurement series affected the deviation from themeasured mean wind speed. It was also investigated if the time of the year had anyinfluence on the measurements.The general conclusion drawn after performing the above-mentioned studies was thatthe NCAR 850 hPa wind speed data and the Reduced-Major-Axis regression methodgave the smallest deviation from the measured mean wind speed in most cases. It wasalso concluded that when a short-term measurement series reached 10 to 14 monthsthere was a significant decrease in deviation from the mean wind speed, regardless ofreference series or method used. Calculations from the model regarding seasonaldependence stated that there was a slight dependency on which period of the year ameasurement was performed. / I processen att bedöma om en plats är lämplig för utbyggnad av vindkraft måste detfinnas övertygande statistiska data som beskriver den genomsnittliga vindhastighetenöver en längre tid. Genom att utföra vindhastighetsmätningar på den tänkta platsenunder en kortare tid, exempelvis ett år, och därefter tillämpas en Measure-Correlate-Predict (MCP) metod i kombination med en långtidsreferens, exempelvis en globalmodell som sträcker sig 20 till 30 år bakåt i tiden kan detta göras. I en MCP-metod kanolika typer av regressionsmetoder användas. När en långtidskorrigerad vinddataseriefinns tillgänglig kan dess beteende på den tänkta platsen analyseras. För att kunna göradetta för flera olika typer av referensserier och regressionsmetoder skapades en modell iMATLAB. Två vinddataserier erhölls från Vattenfall Vindkraft. Dessa var Ringhals ochOskarshamn med högsta mäthöjd på 96 respektive 100 meter. En ytterligarevinddataserie erhölls av Uppsala Universitet från en mätmast nära Marsta med högstamäthöjd på 29 meter.Det fanns flera metoder tillgängliga för att skapa de långtidskorrigeradevinddataserierna. I det här examensarbetet har metoderna Ordinary-Least-Square-,Least-Absolute-Deviation- och Reduced-Major-Axis regressioner använts. För varjemetod testades tre referensserier i kombination med de kortare vinddataserierna. Dessavar NCAR 850 hPa vindhastigheter, NCAR 42 meters sigmanivå vindhastigheter ochannan meteorologisk data.Regressionsmetoderna utvärderades genom att avvikelsen från de kortare mätseriernasmedelvindhastigheter beräknades. Det undersöktes också hur längden på användvinddata från de kortare mätserierna påverkade avvikelsen i medelvindhastighet och omdet fanns något säsongsberoende på när under året som mätningen av vinddata vargjord.Slutsatserna från undersökningarna var att NCAR 850 hPa vindhastigheter ochregressionsmetoden Reduced-Major-Axis generellt gav de lägsta avvikelserna frånuppmätt medelvindhastighet. Slutsatser kunde också dras om längden av användmätdata. Det var tydligt att oavsett referensserie och regressionsmetod uppstod enminskningen i avvikelse från medelvindhastigheten mellan 10 till 14 månaders längd påmätserien. Resultat angående säsongsberoende kunde påvisas i form av avvikelsermellan mätningar gjorda under olika tidpunkter på året. Storlek och tecken påavvikelsen berodde på vilken referensserien i kombination med regressionsmetod somanvändes.