This study investigates how well the Regional Atmospheric Modelling System (RAMS) simulates the sea breeze from False Bay (False Bay sea breeze) at a small resolution of 200m. It describes the influence of the sea breeze in the Stellenbosch wine growing district focusing on temperature, relative humidity and wind speed and direction through three case studies, using three different synoptic conditions. The RAMS simulations are verified against measurements done by automatic weather stations in the study area for all three case studies. The first synoptic condition investigated is when light onshore flow occurred over the south-western Cape. The RAMS model simulated the vertical and horizontal structure of the sea breeze from False Bay very well. However RAMS predicted the onset of the sea breeze 3 hours earlier than the AWS data predicted. The flow was off-shore in the second case study. The RAMS simulations as well as the observed data from the automatic weather stations, showed the two sea breezes influencing the study area, one from Table Bay, west of Stellenbosch, and one from False Bay. In this case study the model simulated the flatter head and stronger False Bay sea breeze. The third case study investigated the influence of strong onshore synoptic conditions, in which the model and observed values showed that no sea breeze developed from False Bay. From the three case studies it was found that the sea breeze is influenced by the synoptic flow and that the sea breeze causes cooling of between 3°C and 16°C and relative humidity (RH) increase of between 16 – 57% depending on the synoptic flow. RAMS was able to simulate the sea breeze theoretically correct and has the potential to be used to identify climatological areas in the wine growing areas of the Western Cape. / Dissertation (MSc)--University of Pretoria, 2007. / Geography, Geoinformatics and Meteorology / Unrestricted

Globally, buildings utilize 35 % of the final energy use and contribute to approximately one-third of CO2 emissions. Hence, reducing the energy use of buildings contributes to a large amount of CO2 emissions to be decreased. The building’s energy use is affected by many parameters, including wind which plays an important role in building energy use. In this thesis, we aim to analyze the impact of wind parameters on building’s energy use on two multi-family building types with natural ventilation at various wind sheltering conditions at different climatic zones in Sweden. Building energy simulation models (BES) of a standalone and an attached building located in Visby, Sweden, were constructed with the use of the dynamic BES IDA ICE. Luleå and Malmö were taken as other two study locations to investigate the impact from different climate zones. The simulations were performed with the constructed calculation models, with the various wind sheltering conditions at the different climatic zones to calculate the energy use of the buildings and ventilation and infiltration losses. The sensitivity analysis was then carried out based on changing the wind profile of the climate file to evaluate the impact of wind on the ventilation and infiltration losses, as well as the heat energy use of the building. The results showed that the energy use for space heating of the attached building was 89 kWh/m2 (38 %) lower than the standalone building. The energy use varies between 9–20 kWh/m2 (3–10 %) considering the exposed, semi-exposed and sheltered wind condition for the two building types. In the different climate zones, Luleå has 47 kWh/m2 higher energy use compared to Visby and Malmö for the standalone building. The corresponding figure for the attached building is 25 kWh/m2. The sensitivity analysis show that when the wind speed is increased by 100 %, the ventilation and infiltration losses increase between 3563–18683 kWh (54–61 %) while the energy use of the building increases between 11–54 kWh/m2 (20–27 %).

No description available.

Many parameters used for Wind Resource Assessment (WRA) have uncertainty and variability, yet are input into the process as single values. The extent of the uncertainty or variance may not be known, and may or may not be significant enough to affect output. This Thesis focused on the energy calculation element of WRA, to assess the affect that errors (uncertainty) in three key user inputs had on the energy results. A parameter was chosen from each of the main groups influencing the energy calculation: wind speed (atmosphere), surface roughness (site conditions), and power curve (turbine technology). Reasonable variation due to uncertainty for wind speed and power curve were taken from other studies and their application simplified. Roughness change was assessed over the 5 classes (Class 0 (water) to 4 (dense forest/city)). WindPRO software was used to calculate the Annual Energy Production (AEP) and applied to three different wind turbine generators at the same coordinate. A sensitivity analysis was done on the AEP results using a hybrid One-At-a-Time Local Sensitivity Analysis by determining percentage changes from baselines and an overall rate of change for those key input parameters. The results showed that roughness class change effect was not linear. Changing from Class 0 to 1, AEP was on average -8±1%. Class 1 to 2 change was on average ‑12±1%. Class 2 to 3 change was on average -20±2%. Class 3 to 4 change was on average -29±2%. The wind speed change effect was found to be roughly linear. If mean wind speed has an error of ±10%, the AEP could be expected to be out by approximately +18/‑17% with a standard deviation of +4/-3%. The power curve change effect was also roughly linear. A PC±9% error leads to an approximate +6/-7% AEP error with a standard deviation of ±1%. Roughness class change was the most sensitive parameter to AEP with a 14.5 average rate of change, followed by wind speed at 1.8, then power curve with a 0.8 rate. Results compared reasonably well with other relevant studies.

This work investigates how to improve wind power predictions using observational wind speed data. Measurements from ultrasonic anemometers (sonics) are available from five of the 22 wind energy turbines at the analysed wind farm in Beeskow, Germany (52°11’48'N, 14°13’E). In addition, measurements from a vertically pointing Doppler lidar (DL) at the Meteorological Observatory Lindenberg - Richard Aßmann Observatory located at a distance of 6 km from the wind farm are evaluated. The LoadManager® tool, developed by LEMSoftware, Leipzig, is used to perform wind power predictions based on different input data for forecasting horizons of 15 min and 30 min. Though wind power predictions have consistently improved in the last decade, persistent reasons for remaining uncertainties are sudden large changes in wind speed, so-called ramp events. The occurrence of ramp events at the wind farm has been investigated. Results on the seasonality of ramp events and their diurnal cycle are presented for multiple ramp detection thresholds. Ramps were found to be most frequent in March and April and least frequent in November and December. Furthermore, ramp events occur more frequently during the day than during the night and for most ramp detection thresholds up-ramp events are slightly more frequent than down-ramp events. For further analysis, the wind power prediction tool is fed with different wind velocity input data. The reference wind power predictions are based on data from a numerical weather prediction (NWP) model. Power predictions using observed wind speed data (DL, sonics) are compared to these reference predictions and evaluated according to two metrics: (i) the absolute difference between the observed and predicted power generation and (ii) the costs incurred due to necessary balancing services. It was found that, (i) the absolute power deviation can be reduced significantly compared to the reference by using power prediction setups based on sonic data. This improvement is even greater during ramp time steps. Power predictions based on the available DL data do not improve the absolute power deviation for the entire data set, albeit they do provide an improvement during down-ramp events. Considering (ii) incurred balancing costs, all power prediction setups based on observational data reduce the balancing costs compared to the reference. Sonic-based configurations yield 75-80% lower balancing costs than the reference and the DL-based setup results in 20% lower balancing costs. / Diese Arbeit untersucht, wie sich Windleistungsprognosen mit Hilfe von Windmesswerten verbessern lassen. Messungen von Ultraschallanemometern (sonics) an Gondeln von fünf der 22 Windenergieanlagen des untersuchten Windparks Beeskow, Deutschland (52°11’48'N, 14°13’E), sind verfügbar. Weiterhin sind Messungen des vertikalgerichteten Doppler Lidars (DL) am Meteorologischen Observatorium Lindenberg - Richard Aßmann Observatorium des DWD verfügbar, welches sich in einer Entfernung von 6km zum Windpark befindet. Das Programm LoadManager® der Leipziger Firma LEM-Software wird für Windleistungsprognosen mit verschiedenen Eingangsdaten für die Prognosezeiträume +15 min und +30min verwendet. Die Qualität von Windleistungsprognosen hat sich in den letzten zehn Jahren stetig verbessert. Unsicherheiten bleiben z.B. sogenannte Windrampen, schnelle, starke Änderungen der Windgeschwindigkeit. Das Auftreten von Windrampen am Windpark Beeskow wurde untersucht und die Ergebnisse werden für verschiedene Rampengrenzwerte vorgestellt. Am häufigsten treten Windrampen im März und April auf und am seltensten treten sie im November und Dezember auf. Außerdem wurden Windrampen häufiger tagsüber als nachts festgestellt. Für die meisten Rampengrenzwerte wurden etwas mehr Leistungsanstiege ('up-ramps') als Leistungsrückgänge ('down-ramps') gefunden. Für weitere Untersuchungen wurden Windleistungsprognosen mit verschiedenen Windgeschwindigkeitsdatensätzen durchgeführt. Als Referenz gelten Windleistungsprognosen auf Basis von Daten numerischer Wettervorhersagemodelle. Windleistungsprognosen auf Basis von Messwerten (sonics, DL) werden mit dem Referenzmodell verglichen und entsprechend zweier Metriken bewertet: (i) der absoluten Abweichung zwischen der vorhergesagten und beobachteten Stromerzeugung und (ii) der für Abweichungen anfallenden Regelenergiekosten. Die Ergebnisse zeigen, dass (i) die absolute Abweichung verglichen mit der Referenz signifikant reduziert werden kann, in dem man Messwerte von sonics für die Leistungsprognose verwendet. Dabei ist die Verbesserung während Windrampen größer als für den gesamten Datensatz. Windleistungsprognosen auf Basis von DL-Daten zeigen keine Verbesserung der Abweichungen für den gesamten Datensatz, jedoch eine signifikante Verbesserung während Leistungsrückgängen. Betrachtet man (ii) die anfallenden Regelenergiekosten, resultieren alle auf Messwerten basierenden Leistungsprognosen in einer Reduktion der Kosten verglichen mit dem Referenzmodell. Windleistungsprognosen auf Basis der Gondelmessungen reduzieren die Regelenergiekosten um 75-80% und Windleistungsprognosen auf DL-Basis ergeben im Mittel etwa 20% niedrigere Regelenergiekosten.

YSCAT is a unique ultrawideband microwave scatterometer developed to investigate the sea surface under a variety of environmental and radar parameters. The YSCAT94 experiment consisted of a six month deployment on the WAVES research tower operated by the Canada Center for inland Waters (CCIW). Over 3500 hours of data were collected at 2Γ 3.05Γ 5.3Γ 10.02Γ and 14 GHz and at a variety of wind speeds, relative azimuth angles, and incidence angle. A low wind speed "rolloff" of the normalized radar cross section (σ°) in YSCAT94 data is found and quantified. The rolloff wind speedΓ γΓ is estimated through regression estimation analysis using an Epanechnikov kernel. For YSCAT94 data, the rolloff is most noticeable at mid-range incidence angles with γ values ranging from 3 to 6 m/s. In order to characterized YSCAT94 backscatter distributions, a second order polynomial in log space is developed as a model for the probability of the radar cross sectionΓρ(σ°). Following Gotwols and ThompsonΓρ(σ°) is found to adhere to a log-normal distribution for horizontal polarization and a generalized log-normal distribution for vertical polarization. If ρ(α|σ°) is assumed to be Rayleigh distributed, the instantaneous amplitude distribution ρ(α) is found to be the integral of a Rayleigh/generalized log-normal distribution. A robust algorithm is developed to fit this probability density function to YSCAT94 backscatter distributions. The mean and variance of the generalized log-normal distribution are derived to facilitate this algorithm. Over 2700 distinct data cases sorted according to five different frequencies, horizontal and vertical polarizations, upwind and downwind, eight different incidence angles Γ1-10 m/s wind speeds, and 0.1-0.38 mean wave slope are considered. Definite trends are recognizable in the fitted parameters a1Γ a2Γ and C of the Rayleigh/generalized log-normal distribution when sorted according to wind speed and mean wave slope. At mid-range incidence angles, the Rayleigh/generalized log-normal distribution is found to adequately characterize both low and high amplitude portions of YSCAT94 backscatter distributions. However, at higher incidence angels (50°and 60°) the more general Weibull/generalized log-normal distributions is found to better characterized the low amplitude portion of the backscatter distributions.

Increasing PM2.5 (particulate matter smaller than 2.5 micrometers) poses a significant health risk to people. Understanding variables critical to PM2.5 spatial and temporal variation is a first step towards protecting vulnerable populations from exposure. Previous studies investigate variables responsible for PM2.5 variation but have a limited temporal span. Moreover, although land-use classes are often taken into account, the vertical environment's influence (e.g., buildings, trees) on PM2.5 concentrations is often ignored and on-road circle buffers are used. To understand variables most critical to PM2.5 concentration variation, an air pollution sensor and GPS unit were affixed to a bicycle to sample for variables over three seasons (spring, summer, fall). Samples were taken on a route during the weekdays at four targeted hours (7AM, 11AM, 3PM, and 7PM) and joined with meteorological data. 3D morphology was assessed using LiDAR data and novel wind-based buffers. Wind speed only, wind direction only, and wind speed and direction buffers were computed and compared for their performance at capturing micro-scale urban morphological variables. Zonal statistics were used to compute morphological indicators under different wind assumptions in seasonal ordinary least squares regression models. A comprehensive wind and buffer performance analysis compares statistical significance for spatial and temporal variation of PM2.5. This study identifies the best wind parameters to use for wind-based buffer generation of urban morphology, which is expected to have implications for buffer design in future studies. Additionally, significant exposure hotspots for UNT students to PM2.5 pollution are identified.

Avaliou-se o desempenho de um modelo numérico de previsão do tempo (GFS - Global Forecast System – antigo AVN – AViatioN model - do Centro Nacional para Previsão Ambiental – NCEP) no prognóstico de variáveis meteorológicas temperatura, déficit de pressão de vapor do ar, saldo de radiação e velocidade do vento, e da evapotranspiração de referência calculada pelos métodos de Thornthwaite (1948) e de Penman-Monteith (Allen et al., 1998). O desempenho foi avaliado por comparação com dados provenientes de uma estação meteorológica, situada em Piracicaba, São Paulo. A temperatura e o déficit de pressão de vapor do ar foram os elementos melhor prognosticados, com desempenho "muito bom" e "bom", de acordo com o índice de desempenho proposto por Camargo e Sentelhas (1997), para no máximo quatro e três dia de antecedência, respectivamente, durante o período seco. Para o período úmido, somente o prognóstico do déficit de pressão de vapor do ar para o primeiro dia mostrou-se "bom". Os prognósticos de saldo de radiação e velocidade do vento foram ruins para ambos os períodos. Em decorrência do bom desempenho do modelo para prognosticar a temperatura, verificou-se que a estimativa de ETo pelo método de Thornthwaite teve boa concordância com o calculado a partir dos dados da estação meteorológica, com antecedência de até três dias para o período seco. Para o úmido, este fato foi observado apenas para o primeiro dia de antecedência. A concordância entre os valores estimados pelo modelo e a partir da estação para o método de Penman-Monteith foi muito baixa, em conseqüência do desempenho do modelo de previsão do tempo em prognosticar o saldo de radiação e a velocidade do vento. / The performance of a numeric weather forecast model (GFS- Forecast System, former AVN - AvatioN model, National Center for Environmental Prediction-NCEP) was evaluated for predicting weather variables, like air temperature and vapor pressure deficit, net radiation and wind speed, as well as reference evapotranspiration calculated by Thornthwaite (1948) and Penman-Monteith (Allen et al., 1948) methods, by the comparison with data obtained by an automatic weather station, in Piracicaba, State of São Paulo, Brazil. Temperature and vapor pressure deficit were the variables predicted with the best accuracy, with a "very good" and "good" performance, according to the index of confidence proposed by Camargo and Sentelhas (1997), for the maximum of four and three days in advance, respectively, during the dry season. For the wet season, only vapor pressure deficit was predicted with a "good" performance of the model. The predictions of net radiation and wind speed were very poor for both seasons. As the weather forecast model predicted temperature well, ETo estimated by Thornthwaite method showed a good agreement with ETo values estimated by observed data from the weather station, with till three days in advance for the dry season. For the wet season, such agreement was observed just for one day in advance. When ETo estimated by Penman-Monteith method with data from the weather forecast model and from weather station were compared any agreement was observed, which was caused by the poor performance of the numeric weather forecast model to predict net radiation and wind speed.

Avaliou-se o desempenho de um modelo numérico de previsão do tempo (GFS - Global Forecast System – antigo AVN – AViatioN model - do Centro Nacional para Previsão Ambiental – NCEP) no prognóstico de variáveis meteorológicas temperatura, déficit de pressão de vapor do ar, saldo de radiação e velocidade do vento, e da evapotranspiração de referência calculada pelos métodos de Thornthwaite (1948) e de Penman-Monteith (Allen et al., 1998). O desempenho foi avaliado por comparação com dados provenientes de uma estação meteorológica, situada em Piracicaba, São Paulo. A temperatura e o déficit de pressão de vapor do ar foram os elementos melhor prognosticados, com desempenho "muito bom" e "bom", de acordo com o índice de desempenho proposto por Camargo e Sentelhas (1997), para no máximo quatro e três dia de antecedência, respectivamente, durante o período seco. Para o período úmido, somente o prognóstico do déficit de pressão de vapor do ar para o primeiro dia mostrou-se "bom". Os prognósticos de saldo de radiação e velocidade do vento foram ruins para ambos os períodos. Em decorrência do bom desempenho do modelo para prognosticar a temperatura, verificou-se que a estimativa de ETo pelo método de Thornthwaite teve boa concordância com o calculado a partir dos dados da estação meteorológica, com antecedência de até três dias para o período seco. Para o úmido, este fato foi observado apenas para o primeiro dia de antecedência. A concordância entre os valores estimados pelo modelo e a partir da estação para o método de Penman-Monteith foi muito baixa, em conseqüência do desempenho do modelo de previsão do tempo em prognosticar o saldo de radiação e a velocidade do vento. / The performance of a numeric weather forecast model (GFS- Forecast System, former AVN - AvatioN model, National Center for Environmental Prediction-NCEP) was evaluated for predicting weather variables, like air temperature and vapor pressure deficit, net radiation and wind speed, as well as reference evapotranspiration calculated by Thornthwaite (1948) and Penman-Monteith (Allen et al., 1948) methods, by the comparison with data obtained by an automatic weather station, in Piracicaba, State of São Paulo, Brazil. Temperature and vapor pressure deficit were the variables predicted with the best accuracy, with a "very good" and "good" performance, according to the index of confidence proposed by Camargo and Sentelhas (1997), for the maximum of four and three days in advance, respectively, during the dry season. For the wet season, only vapor pressure deficit was predicted with a "good" performance of the model. The predictions of net radiation and wind speed were very poor for both seasons. As the weather forecast model predicted temperature well, ETo estimated by Thornthwaite method showed a good agreement with ETo values estimated by observed data from the weather station, with till three days in advance for the dry season. For the wet season, such agreement was observed just for one day in advance. When ETo estimated by Penman-Monteith method with data from the weather forecast model and from weather station were compared any agreement was observed, which was caused by the poor performance of the numeric weather forecast model to predict net radiation and wind speed.

The humidity, temperature, wind speed/direction micro sensors can be manufactured individually, resulting in three individual substrates. The integration of the three sensors into a single substrate is a vital challenge to achieve an integrated intelligent sensor so called a multiple sensor. This requires the integration of process flows and is a major challenge because adequate sensor performance must be maintained. Polyimide was selected as a humidity sensing material for its compatibility with conventional integrated circuit fabrication technology, negligible temperature dependence and good resistance against contamination. Nickel was selected for the temperature and wind speed/direction sensor because of its useful temperature coefficient and the advantage of its cost. Since the known wet etchant for nickel requires hard-baked photoresist, a method which does not attack the polyimide while removing the photoresist must be developed. The method developed for etching nickel employs hard-bake-free photoresist. Other challenge was ensuring good thermal isolation for the wind speed/direction sensor fabricated on a silicon nitride layer preformed on top of a silicon wafer. Since silicon acts as a good heat sink, the silicon under the sensor was etched entirely away until the silicon nitride layer was reached. This structure achieved good thermal isolation resulting in small power consumption. This low power feature is essential for sensors deployed in fields where power access or replacement of power sources is restricted. This structure was compared with the structure created by polyimide plateau on a silicon nitride layer coated on a silicon substrate as a function of power consumption. Based on the examination of thermal isolation, the multiple sensor utilizing a MEMS technique was fabricated with a single-sided mask aligner. The characteristics of humidity sensors fabricated with polyimide were examined in detail with respect to variations of electrode structures, improvement of sensitivity, effect of process temperature, temperature and frequency dependence, and stability. The humidity sensor constructed with O2 plasma treated polyimide resulted an improvement in sensitivity and hysteresis. The investigation using XPS, FTIR and AFM concluded the chemical modification of polyimide played an important role in this improvement. The design, fabrication and results of a series of humidity sensors are quantified. There is always no unique packaging solution for sensors because of the application-specific nature of the sensors. This intelligent environmental monitoring system was designed to accommodate both an environmental sensor and its signal conditioning electronics circuitry (SICONEC) into a single package. The environmental sensors need direct exposure to the environment while SICONEC needs a sealed encapsulation to avoid environmental damage. A new style of packaging addressing these requirements was demonstrated using a hot embossing machine. The hot embossing machine was used to embed an integrated circuit (IC) in a bare die condition into a polycarbonate (PC) sheet. In this case, the IC was flipped down against the PC, which protects the front side of the IC from the environmental damages. In a test phase, a die containing operational amplifiers was embossed into the PC. A humidity sensor and surface mount resisters were placed on the same surface of the PC to test the validity of this new technique. Interconnection between the embossed die and the humidity sensor was established using bonding wires. Copper tracks were also used to ensure all electrical connections for the die, the humidity sensor and the resistors. The results clarified the method developed. Details of process methods, issues and further potential improvement are reported.