983 resultados para Wind speed extrapolation


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The transfer coefficients for momentum and heat have been determined for 10 m neutral wind speeds (U-10n) between 0 and 12 m/s using data from the Surface of the Ocean, Fluxes and Interactions with the Atmosphere (SOFIA) and Structure des Echanges Mer-Atmosphere, Proprietes des Heterogeneites Oceaniques: Recherche Experimentale (SEMAPHORE) experiments. The inertial dissipation method was applied to wind and pseudo virtual temperature spectra from a sonic anemometer, mounted on a platform (ship) which was moving through the turbulence held. Under unstable conditions the assumptions concerning the turbulent kinetic energy (TKE) budget appeared incorrect. Using a bulk estimate for the stability parameter, Z/L (where Z is the height and L is the Obukhov length), this resulted in anomalously low drag coefficients compared to neutral conditions. Determining Z/L iteratively, a low rate of convergence was achieved. It was concluded that the divergence of the turbulent transport of TKE was not negligible under unstable conditions. By minimizing the dependence of the calculated neutral drag coefficient on stability, this term was estimated at about -0.65Z/L. The resulting turbulent fluxes were then in close agreement with other studies at moderate wind speed. The drag and exchange coefficients for low wind speeds were found to be C-en x 10(3) = 2.79U(10n)(-1) + 0.66 (U-10n < 5.2 m/s), C-en x 10(3) = C-hn x 10(3) = 1.2 (U-10n greater than or equal to 5.2 m/s), and C-dn x 10(3) = 11.7U(10n)(-2) + 0.668 (U-10n < 5.5 m/s), which imply a rapid increase of the coefficient values as the wind decreased within the smooth flow regime. The frozen turbulence hypothesis and the assumptions of isotropy and an inertial subrange were found to remain valid at these low wind speeds for these shipboard measurements. Incorporation of a free convection parameterization had little effect.

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Suite à un stage avec la compagnie Hatch, nous possédons des jeux de données composés de séries chronologiques de vitesses de vent mesurées à divers sites dans le monde, sur plusieurs années. Les ingénieurs éoliens de la compagnie Hatch utilisent ces jeux de données conjointement aux banques de données d’Environnement Canada pour évaluer le potentiel éolien afin de savoir s’il vaut la peine d’installer des éoliennes à ces endroits. Depuis quelques années, des compagnies offrent des simulations méso-échelle de vitesses de vent, basées sur divers indices environnementaux de l’endroit à évaluer. Les ingénieurs éoliens veulent savoir s’il vaut la peine de payer pour ces données simulées, donc si celles-ci peuvent être utiles lors de l’estimation de la production d’énergie éolienne et si elles pourraient être utilisées lors de la prévision de la vitesse du vent long terme. De plus, comme l’on possède des données mesurées de vitesses de vent, l’on en profitera pour tester à partir de diverses méthodes statistiques différentes étapes de l’estimation de la production d’énergie. L’on verra les méthodes d’extrapolation de la vitesse du vent à la hauteur d’une turbine éolienne et l’on évaluera ces méthodes à l’aide de l’erreur quadratique moyenne. Aussi, on étudiera la modélisation de la vitesse du vent par la distributionWeibull et la variation de la distribution de la vitesse dans le temps. Finalement, l’on verra à partir de la validation croisée et du bootstrap si l’utilisation de données méso-échelle est préférable à celle de données des stations de référence, en plus de tester un modèle où les deux types de données sont utilisées pour prédire la vitesse du vent. Nous testerons la méthodologie globale présentement utilisée par les ingénieurs éoliens pour l’estimation de la production d’énergie d’un point de vue statistique, puis tenterons de proposer des changements à cette méthodologie, qui pourraient améliorer l’estimation de la production d’énergie annuelle.

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High-speed solar wind streams modify the Earth's geomagnetic environment, perturbing the ionosphere, modulating the flux of cosmic rays into the Earth atmosphere, and triggering substorms. Such activity can affect modern technological systems. To investigate the potential for predicting the arrival of such streams at Earth, images taken by the Heliospheric Imager (HI) on the STEREO-A spacecraft have been used to identify the onsets of high-speed solar wind streams from observations of regions of increased plasma concentrations associated with corotating interaction regions, or CIRs. In order to confirm that these transients were indeed associated with CIRs and to study their average properties, arrival times predicted from the HI images were used in a superposed epoch analysis to confirm their identity in near-Earth solar wind data obtained by the Advanced Composition Explorer (ACE) spacecraft and to observe their influence on a number of salient geophysical parameters. The results are almost identical to those of a parallel superposed epoch analysis that used the onset times of the high-speed streams derived from east/west deflections in the ACE measurements of solar wind speed to predict the arrival of such streams at Earth, assuming they corotated with the Sun with a period of 27 days. Repeating the superposed epoch analysis using restricted data sets demonstrates that this technique can provide a timely prediction of the arrival of CIRs at least 1 day ahead of their arrival at Earth and that such advanced warning can be provided from a spacecraft placed 40° ahead of Earth in its orbit.

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The variability of hourly values of solar wind number density, number density variation, speed, speed variation and dynamic pressure with IMF Bz and magnitude |B| has been examined for the period 1965–1986. We wish to draw attention to a strong correlation in number density and number density fluctuation with IMF Bz characterised by a symmetric increasing trend in these quantities away from Bz = 0 nT. The fluctuation level in solar wind speed is found to be relatively independent of Bz. We infer that number density and number density variability dominate in controlling solar wind dynamic pressure and dynamic pressure variability. It is also found that dynamic pressure is correlated with each component of IMF and that there is evidence of morphological differences between the variation with each component. Finally, we examine the variation of number density, speed, dynamic pressure and fluctuation level in number density and speed with IMF magnitude |B|. Again we find that number density variation dominates over solar wind speed in controlling dynamic pressure.