Inertial dissipation method applied to derive turbulent fluxed over the ocean during the Surface of the Ocean, Fluxes and Interactions with the Atmosphere Atlantic Stratocumulus Transition Experiment (SOFIA/ASTEX) and Structure des Echanges Mer-Atmosphere, Proprietes des Heterogeneites Oceaniques: Recherche Experimentale (SEMAPHORE) experiments with low to moderate wind speed

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.

Évaluation de la modélisation et des prévisions de la vitesse du vent menant à l'estimation de la production d'énergie annuelle d'une turbine éolienne

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.

CFD modelling of wind farms in complex terrain

Modelling of entire wind farms in flat and complex terrain using a full 3D Navier–Stokes solver for incompressible flow is presented in this paper. Numerical integration of the governing equations is performed using an implicit pressure correction scheme, where the wind turbines (W/Ts) are modelled as momentum absorbers through their thrust coefficient. The k–ω turbulence model, suitably modified for atmospheric flows, is employed for closure. A correction is introduced to account for the underestimation of the near wake deficit, in which the turbulence time scale is bounded using a general “realizability” constraint for the fluctuating velocities. The second modelling issue that is discussed in this paper is related to the determination of the reference wind speed for the thrust calculation of the machines. Dealing with large wind farms and wind farms in complex terrain, determining the reference wind speed is not obvious when a W/T operates in the wake of another WT and/or in complex terrain. Two alternatives are compared: using the wind speed value at hub height one diameter upstream of the W/T and adopting an induction factor-based concept to overcome the utilization of a wind speed at a certain distance upwind of the rotor. Application is made in two wind farms, a five-machine one located in flat terrain and a 43-machine one located in complex terrain.

Modelling wake effects in large wind farms in complex terrain: the problem, the methods and the issues

Computational fluid dynamic (CFD) methods are used in this paper to predict the power production from entire wind farms in complex terrain and to shed some light into the wake flow patterns. Two full three-dimensional Navier–Stokes solvers for incompressible fluid flow, employing k − ϵ and k − ω turbulence closures, are used. The wind turbines are modeled as momentum absorbers by means of their thrust coefficient through the actuator disk approach. Alternative methods for estimating the reference wind speed in the calculation of the thrust are tested. The work presented in this paper is part of the work being undertaken within the UpWind Integrated Project that aims to develop the design tools for next generation of large wind turbines. In this part of UpWind, the performance of wind farm and wake models is being examined in complex terrain environment where there are few pre-existing relevant measurements. The focus of the work being carried out is to evaluate the performance of CFD models in large wind farm applications in complex terrain and to examine the development of the wakes in a complex terrain environment.

A sensitivity study of the WRF model in wind simulation for an area of high wind energy

The performance of the Weather Research and Forecast (WRF) model in wind simulation was evaluated under different numerical and physical options for an area of Portugal, located in complex terrain and characterized by its significant wind energy resource. The grid nudging and integration time of the simulations were the tested numerical options. Since the goal is to simulate the near-surface wind, the physical parameterization schemes regarding the boundary layer were the ones under evaluation. Also, the influences of the local terrain complexity and simulation domain resolution on the model results were also studied. Data from three wind measuring stations located within the chosen area were compared with the model results, in terms of Root Mean Square Error, Standard Deviation Error and Bias. Wind speed histograms, occurrences and energy wind roses were also used for model evaluation. Globally, the model accurately reproduced the local wind regime, despite a significant underestimation of the wind speed. The wind direction is reasonably simulated by the model especially in wind regimes where there is a clear dominant sector, but in the presence of low wind speeds the characterization of the wind direction (observed and simulated) is very subjective and led to higher deviations between simulations and observations. Within the tested options, results show that the use of grid nudging in simulations that should not exceed an integration time of 2 days is the best numerical configuration, and the parameterization set composed by the physical schemes MM5–Yonsei University–Noah are the most suitable for this site. Results were poorer in sites with higher terrain complexity, mainly due to limitations of the terrain data supplied to the model. The increase of the simulation domain resolution alone is not enough to significantly improve the model performance. Results suggest that error minimization in the wind simulation can be achieved by testing and choosing a suitable numerical and physical configuration for the region of interest together with the use of high resolution terrain data, if available.

Learning wind fields with multiple kernels

This paper presents multiple kernel learning (MKL) regression as an exploratory spatial data analysis and modelling tool. The MKL approach is introduced as an extension of support vector regression, where MKL uses dedicated kernels to divide a given task into sub-problems and to treat them separately in an effective way. It provides better interpretability to non-linear robust kernel regression at the cost of a more complex numerical optimization. In particular, we investigate the use of MKL as a tool that allows us to avoid using ad-hoc topographic indices as covariables in statistical models in complex terrains. Instead, MKL learns these relationships from the data in a non-parametric fashion. A study on data simulated from real terrain features confirms the ability of MKL to enhance the interpretability of data-driven models and to aid feature selection without degrading predictive performances. Here we examine the stability of the MKL algorithm with respect to the number of training data samples and to the presence of noise. The results of a real case study are also presented, where MKL is able to exploit a large set of terrain features computed at multiple spatial scales, when predicting mean wind speed in an Alpine region.

Multiple Kernel Learning of Environmental Data. Case study: Analysis and Mapping of Wind Fields

The paper presents the Multiple Kernel Learning (MKL) approach as a modelling and data exploratory tool and applies it to the problem of wind speed mapping. Support Vector Regression (SVR) is used to predict spatial variations of the mean wind speed from terrain features (slopes, terrain curvature, directional derivatives) generated at different spatial scales. Multiple Kernel Learning is applied to learn kernels for individual features and thematic feature subsets, both in the context of feature selection and optimal parameters determination. An empirical study on real-life data confirms the usefulness of MKL as a tool that enhances the interpretability of data-driven models.

Effect of damper and heat source on wind catcher natural ventilation performance

Experimental wind tunnel and smoke visualisation testing and CFD modelling were conducted to investigate the effect of air flow control mechanism and heat source inside rooms on wind catchers/towers performance. For this purpose, a full-scale wind catcher was connected to a test room and positioned centrally in an open boundary wind tunnel. Pressure coefficients (C-p's) around the wind catcher and air flow into the test room were established. The performance of the wind catcher depends greatly on the wind speed and direction. The incorporation of dampers and egg crate grille at ceiling level reduces and regulates the air flow rate with an average pressure loss coefficient of 0.01. The operation of the wind catcher in the presence of heat sources will potentially lower the internal temperatures in line with the external temperatures.

Predicting the arrival of high-speed solar wind streams at Earth using the STEREO Heliospheric Imagers

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.

Evaluation of regional climate simulations for air quality modelling purposes

In order to evaluate the future potential benefits of emission regulation on regional air quality, while taking into account the effects of climate change, off-line air quality projection simulations are driven using weather forcing taken from regional climate models. These regional models are themselves driven by simulations carried out using global climate models (GCM) and economical scenarios. Uncertainties and biases in climate models introduce an additional “climate modeling” source of uncertainty that is to be added to all other types of uncertainties in air quality modeling for policy evaluation. In this article we evaluate the changes in air quality-related weather variables induced by replacing reanalyses-forced by GCM-forced regional climate simulations. As an example we use GCM simulations carried out in the framework of the ERA-interim programme and of the CMIP5 project using the Institut Pierre-Simon Laplace climate model (IPSLcm), driving regional simulations performed in the framework of the EURO-CORDEX programme. In summer, we found compensating deficiencies acting on photochemistry: an overestimation by GCM-driven weather due to a positive bias in short-wave radiation, a negative bias in wind speed, too many stagnant episodes, and a negative temperature bias. In winter, air quality is mostly driven by dispersion, and we could not identify significant differences in either wind or planetary boundary layer height statistics between GCM-driven and reanalyses-driven regional simulations. However, precipitation appears largely overestimated in GCM-driven simulations, which could significantly affect the simulation of aerosol concentrations. The identification of these biases will help interpreting results of future air quality simulations using these data. Despite these, we conclude that the identified differences should not lead to major difficulties in using GCM-driven regional climate simulations for air quality projections.

Short-term variability of solar wind number density, speed and dynamic pressure as a function of the interplanetary magnetic field components: A survey over two solar cycles

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.

On the ability of statistical wind-wave models to capture the variability and long-term trends of the North Atlantic winter wave climate

A dynamical wind-wave climate simulation covering the North Atlantic Ocean and spanning the whole 21st century under the A1B scenario has been compared with a set of statistical projections using atmospheric variables or large scale climate indices as predictors. As a first step, the performance of all statistical models has been evaluated for the present-day climate; namely they have been compared with a dynamical wind-wave hindcast in terms of winter Significant Wave Height (SWH) trends and variance as well as with altimetry data. For the projections, it has been found that statistical models that use wind speed as independent variable predictor are able to capture a larger fraction of the winter SWH inter-annual variability (68% on average) and of the long term changes projected by the dynamical simulation. Conversely, regression models using climate indices, sea level pressure and/or pressure gradient as predictors, account for a smaller SWH variance (from 2.8% to 33%) and do not reproduce the dynamically projected long term trends over the North Atlantic. Investigating the wind-sea and swell components separately, we have found that the combination of two regression models, one for wind-sea waves and another one for the swell component, can improve significantly the wave field projections obtained from single regression models over the North Atlantic.

Sensitivity of the WRF model to PBL parametrisations and nesting techniques: evaluation of wind storms over complex terrain

Simulating surface wind over complex terrain is a challenge in regional climate modelling. Therefore, this study aims at identifying a set-up of the Weather Research and Forecasting Model (WRF) model that minimises system- atic errors of surface winds in hindcast simulations. Major factors of the model configuration are tested to find a suitable set-up: the horizontal resolution, the planetary boundary layer (PBL) parameterisation scheme and the way the WRF is nested to the driving data set. Hence, a number of sensitivity simulations at a spatial resolution of 2 km are carried out and compared to observations. Given the importance of wind storms, the analysis is based on case studies of 24 historical wind storms that caused great economic damage in Switzerland. Each of these events is downscaled using eight different model set-ups, but sharing the same driving data set. The results show that the lack of representation of the unresolved topography leads to a general overestimation of wind speed in WRF. However, this bias can be substantially reduced by using a PBL scheme that explicitly considers the effects of non-resolved topography, which also improves the spatial structure of wind speed over Switzerland. The wind direction, although generally well reproduced, is not very sensitive to the PBL scheme. Further sensitivity tests include four types of nesting methods: nesting only at the boundaries of the outermost domain, analysis nudging, spectral nudging, and the so-called re-forecast method, where the simulation is frequently restarted. These simulations show that restricting the freedom of the model to develop large-scale disturbances slightly increases the temporal agreement with the observations, at the same time that it further reduces the overestimation of wind speed, especially for maximum wind peaks. The model performance is also evaluated in the outermost domains, where the resolution is coarser. The results demonstrate the important role of horizontal resolution, where the step from 6 to 2 km significantly improves model performance. In summary, the combination of a grid size of 2 km, the non-local PBL scheme modified to explicitly account for non-resolved orography, as well as analysis or spectral nudging, is a superior combination when dynamical downscaling is aimed at reproducing real wind fields.