The abundance of gray whales along the coast of Flores Island, BC, varies on an annual basis. This thesis searches for a relationship between gray whale abundance in this area and environmental forcing factors. Regression analysis was used to search for relationships, using gray whale abundance as the dependent variable and sea-surface temperature, salinity, wind speed, upwelling indices and hours of bright sunlight. Independent variables were also lagged against gray whale abundance to search for time lags between variables. When combine in a multiple regression model, wind speed and upwelling lagged two years explained 89.6% (p = 0.004) of the variance in gray whale abundance. A possible pathway for this relationship may exist through local kelp populations, which have the ability to affect gray whale prey abundance.

Towards advancing the indices of hurricane energetics that are associated with potential damage, we develop a new methodology for calculating integrated kinetic energy (IKE) climatology. A simple, observation and dynamical - based radial wind speed model is used with the Extended Best Track Data Set to calculate IKE for North Atlantic Hurricanes from 1988 to 2008. The method is evaluated against previous methods of tropical cyclone intensity analysis, and the results are compared to traditional indices in terms of characterizing storm energetics and relating to storm surge. It is shown that the traditional indices are inaccurate measurements of hurricane energetics, and the assumptions that they are based on are not valid. Furthermore, in analyzing storm surge, it is possible that tropical cyclone damage is more strongly correlated with IKE rather than maximum wind speed.

Made available in DSpace on 2015-05-08T14:57:18Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 1906808 bytes, checksum: c9f35f40f740e498e4aa3392c91ed370 (MD5) Previous issue date: 2014-02-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This work is in the context of distributed micro-generation through the use of wind power in urban areas. Generally, the installation of wind power generation systems is preceded by an analysis of the potential generation. From a study of methods and technologies for monitoring the wind velocity, it was decided to develop a monitoring system formed by a network of 10 sensor nodes capable of recovering energy from the environment for its operation, avoiding the need for batteries or connection to the power network. Each sensor node is comprised of a micro-turbine that feeds a DC generator, and an RF transceiver for sending data to a processing center. While providing a voltage dependent on wind speed, the set micro-turbine generator provides the energy necessary for the operation of the transceiver. The performance of the prototype was evaluated by means of a wind tunnel developed during the work. The system included a data acquisition board with microcontroller to measure the voltage at the output of the generator (in different load conditions), a reference anemometer to measure the wind speed in the tunnel, and a computer for acquisition and processing of the experimental data. The developed sensor node is able to operate with speeds greater than 3.5 m/s, threshold near the operating threshold of wind turbines for distributed generation wind (3.0 m/s). Keywords: Wind Micro-turbine, Monitoring Wind Speed, Sensor Node. / Este trabalho se insere no contexto da micro-geração distribuída através do aproveitamento da energia eólica em áreas urbanas. Geralmente, a instalação de sistemas de geração eólica é precedida por uma análise do potencial de geração. A partir de um estudo de métodos e tecnologias para o monitoramento da velocidade do vento, decidiu-se por desenvolver um sistema de monitoramento formado por uma rede de 10 nós sensores capazes de recuperar energia do ambiente para seu funcionamento, de forma a evitar a necessidade de baterias ou conexão à rede elétrica. Cada nó sensor é composto por uma microturbina que alimenta um gerador CC, e um transceptor RF para envio dos dados a uma central de processamento. Ao mesmo tempo em que fornece uma tensão dependente da velocidade do vento, o conjunto microturbina-gerador fornece a energia necessária ao funcionamento do transceptor. O desempenho do protótipo desenvolvido foi avaliado por intermédio de um túnel de vento desenvolvido ao longo do trabalho. O sistema de aquisição de dados incluiu uma placa com microcontrolador para medição da tensão na saída do gerador (em diferentes condições de carga), um anemômetro de referência para medição da velocidade do vento no túnel, e um computador para aquisição e processamento dos dados experimentais. O nó sensor desenvolvido é capaz de funcionar com velocidades do vento maiores que 3,5 m/s, limiar próximo do limiar de funcionamento das turbinas eólicas para geração distribuída (3,0 m/s).

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.