A study into the accuracy of using meteorological wind data to estimate turbine generation output

Meteorological (met) station data is used as the basis for a number of influential studies into the impacts of the variability of renewable resources. Real turbine output data is not often easy to acquire, whereas meteorological wind data, supplied at a standardised height of 10 m, is widely available. This data can be extrapolated to a standard turbine height using the wind profile power law and used to simulate the hypothetical power output of a turbine. Utilising a number of met sites in such a manner can develop a model of future wind generation output. However, the accuracy of this extrapolation is strongly dependent on the choice of the wind shear exponent alpha. This paper investigates the accuracy of the simulated generation output compared to reality using a wind farm in North Rhins, Scotland and a nearby met station in West Freugh. The results show that while a single annual average value for alpha may be selected to accurately represent the long term energy generation from a simulated wind farm, there are significant differences between simulation and reality on an hourly power generation basis, with implications for understanding the impact of variability of renewables on short timescales, particularly system balancing and the way that conventional generation may be asked to respond to a high level of variable renewable generation on the grid in the future.

Correction of atmospheric delay effects in radar interferometry using a nested mesoscale atmospheric model

The temporal variability of the atmosphere through which radio waves pass in the technique of differential radar interferometry can seriously limit the accuracy with which the method can measure surface motion. A forward, nested mesoscale model of the atmosphere can be used to simulate the variable water content along the radar path and the resultant phase delays. Using this approach we demonstrate how to correct an interferogram of Mount Etna in Sicily associated with an eruption in 2004-5. The regional mesoscale model (Unified Model) used to simulate the atmosphere at higher resolutions consists of four nested domains increasing in resolution (12, 4, 1, 0.3 km), sitting within the analysis version of a global numerical model that is used to initiate the simulation. Using the high resolution 3D model output we compute the surface pressure, temperature and the water vapour, liquid and solid water contents, enabling the dominant hydrostatic and wet delays to be calculated at specific times corresponding to the acquisition of the radar data. We can also simulate the second-order delay effects due to liquid water and ice.

The use of land-sea warming contrast under climate change to improve impact metrics

A favoured method of assimilating information from state-of-the-art climate models into integrated assessment models of climate impacts is to use the transient climate response (TCR) of the climate models as an input, sometimes accompanied by a pattern matching approach to provide spatial information. More recent approaches to the problem use TCR with another independent piece of climate model output: the land-sea surface warming ratio (φ). In this paper we show why the use of φ in addition to TCR has such utility. Multiple linear regressions of surface temperature change onto TCR and φ in 22 climate models from the CMIP3 multi-model database show that the inclusion of φ explains a much greater fraction of the inter-model variance than using TCR alone. The improvement is particularly pronounced in North America and Eurasia in the boreal summer season, and in the Amazon all year round. The use of φ as the second metric is beneficial for three reasons: firstly it is uncorrelated with TCR in state-of-the-art climate models and can therefore be considered as an independent metric; secondly, because of its projected time-invariance, the magnitude of φ is better constrained than TCR in the immediate future; thirdly, the use of two variables is much simpler than approaches such as pattern scaling from climate models. Finally we show how using the latest estimates of φ from climate models with a mean value of 1.6—as opposed to previously reported values of 1.4—can significantly increase the mean time-integrated discounted damage projections in a state-of-the-art integrated assessment model by about 15 %. When compared to damages calculated without the inclusion of the land-sea warming ratio, this figure rises to 65 %, equivalent to almost 200 trillion dollars over 200 years.

On the potential of assimilating meteorological analyses in a global climate model for the purpose of model validation

A simple four-dimensional assimilation technique, called Newtonian relaxation, has been applied to the Hamburg climate model (ECHAM), to enable comparison of model output with observations for short periods of time. The prognostic model variables vorticity, divergence, temperature, and surface pressure have been relaxed toward European Center for Medium-Range Weather Forecasts (ECMWF) global meteorological analyses. Several experiments have been carried out, in which the values of the relaxation coefficients have been varied to find out which values are most usable for our purpose. To be able to use the method for validation of model physics or chemistry, good agreement of the model simulated mass and wind field is required. In addition, the model physics should not be disturbed too strongly by the relaxation forcing itself. Both aspects have been investigated. Good agreement with basic observed quantities, like wind, temperature, and pressure is obtained for most simulations in the extratropics. Derived variables, like precipitation and evaporation, have been compared with ECMWF forecasts and observations. Agreement for these variables is smaller than for the basic observed quantities. Nevertheless, considerable improvement is obtained relative to a control run without assimilation. Differences between tropics and extratropics are smaller than for the basic observed quantities. Results also show that precipitation and evaporation are affected by a sort of continuous spin-up which is introduced by the relaxation: the bias (ECMWF-ECHAM) is increasing with increasing relaxation forcing. In agreement with this result we found that with increasing relaxation forcing the vertical exchange of tracers by turbulent boundary layer mixing and, in a lesser extent, by convection, is reduced.

Assessment of a 1 hour gridded precipitation dataset to drive a hydrological model: a case study of the summer 2007 floods in the Upper Severn, UK

In this study a gridded hourly 1-km precipitation dataset for a meso-scale catchment (4,062 km2) of the Upper Severn River, UK was constructed using rainfall radar data to disaggregate a daily precipitation (rain gauge) dataset. The dataset was compared to an hourly precipitation dataset created entirely from rainfall radar data. Results found that when assessed against gauge readings and as input to the Lisflood-RR hydrological model, the rain gauge/radar disaggregated dataset performed the best suggesting that this simple method of combining rainfall radar data with rain gauge readings can provide temporally detailed precipitation datasets for calibrating hydrological models.

Metabifurcation analysis of a mean field model of the cortex

Mean field models (MFMs) of cortical tissue incorporate salient, average features of neural masses in order to model activity at the population level, thereby linking microscopic physiology to macroscopic observations, e.g., with the electroencephalogram (EEG). One of the common aspects of MFM descriptions is the presence of a high-dimensional parameter space capturing neurobiological attributes deemed relevant to the brain dynamics of interest. We study the physiological parameter space of a MFM of electrocortical activity and discover robust correlations between physiological attributes of the model cortex and its dynamical features. These correlations are revealed by the study of bifurcation plots, which show that the model responses to changes in inhibition belong to two archetypal categories or “families”. After investigating and characterizing them in depth, we discuss their essential differences in terms of four important aspects: power responses with respect to the modeled action of anesthetics, reaction to exogenous stimuli such as thalamic input, and distributions of model parameters and oscillatory repertoires when inhibition is enhanced. Furthermore, while the complexity of sustained periodic orbits differs significantly between families, we are able to show how metamorphoses between the families can be brought about by exogenous stimuli. We here unveil links between measurable physiological attributes of the brain and dynamical patterns that are not accessible by linear methods. They instead emerge when the nonlinear structure of parameter space is partitioned according to bifurcation responses. We call this general method “metabifurcation analysis”. The partitioning cannot be achieved by the investigation of only a small number of parameter sets and is instead the result of an automated bifurcation analysis of a representative sample of 73,454 physiologically admissible parameter sets. Our approach generalizes straightforwardly and is well suited to probing the dynamics of other models with large and complex parameter spaces.

A Lagrangian model of air-mass photochemistry and mixing using a trajectory ensemble: the Cambridge Tropospheric Trajectory model of Chemistry And Transport (CiTTyCAT) version 4.2

A Lagrangian model of photochemistry and mixing is described (CiTTyCAT, stemming from the Cambridge Tropospheric Trajectory model of Chemistry And Transport), which is suitable for transport and chemistry studies throughout the troposphere. Over the last five years, the model has been developed in parallel at several different institutions and here those developments have been incorporated into one "community" model and documented for the first time. The key photochemical developments include a new scheme for biogenic volatile organic compounds and updated emissions schemes. The key physical development is to evolve composition following an ensemble of trajectories within neighbouring air-masses, including a simple scheme for mixing between them via an evolving "background profile", both within the boundary layer and free troposphere. The model runs along trajectories pre-calculated using winds and temperature from meteorological analyses. In addition, boundary layer height and precipitation rates, output from the analysis model, are interpolated to trajectory points and used as inputs to the mixing and wet deposition schemes. The model is most suitable in regimes when the effects of small-scale turbulent mixing are slow relative to advection by the resolved winds so that coherent air-masses form with distinct composition and strong gradients between them. Such air-masses can persist for many days while stretching, folding and thinning. Lagrangian models offer a useful framework for picking apart the processes of air-mass evolution over inter-continental distances, without being hindered by the numerical diffusion inherent to global Eulerian models. The model, including different box and trajectory modes, is described and some output for each of the modes is presented for evaluation. The model is available for download from a Subversion-controlled repository by contacting the corresponding authors.

Regional changes in wind energy potential over Europe using regional climate model ensemble projections

The impact of climate change on wind power generation potentials over Europe is investigated by considering ensemble projections from two regional climate models (RCMs) driven by a global climate model (GCM). Wind energy density and its interannual variability are estimated based on hourly near-surface wind speeds. Additionally, the possible impact of climatic changes on the energy output of a sample 2.5-MW turbine is discussed. GCM-driven RCM simulations capture the behavior and variability of current wind energy indices, even though some differences exist when compared with reanalysis-driven RCM simulations. Toward the end of the twenty-first century, projections show significant changes of energy density on annual average across Europe that are substantially stronger in seasonal terms. The emergence time of these changes varies from region to region and season to season, but some long-term trends are already statistically significant in the middle of the twenty-first century. Over northern and central Europe, the wind energy potential is projected to increase, particularly in winter and autumn. In contrast, energy potential over southern Europe may experience a decrease in all seasons except for the Aegean Sea. Changes for wind energy output follow the same patterns but are of smaller magnitude. The GCM/RCM model chains project a significant intensification of both interannual and intra-annual variability of energy density over parts of western and central Europe, thus imposing new challenges to a reliable pan-European energy supply in future decades.

Mobile-to-mobile MIMO transmit-receive diversity systems: analysis and performance in three-dimensional double-correlated channels

Mobile-to-mobile (M-to-M) communications are expected to play a crucial role in future wireless systems and networks. In this paper, we consider M-to-M multiple-input multiple-output (MIMO) maximal ratio combining system and assess its performance in spatially correlated channels. The analysis assumes double-correlated Rayleigh-and-Lognormal fading channels and is performed in terms of average symbol error probability, outage probability, and ergodic capacity. To obtain the receive and transmit spatial correlation functions needed for the performance analysis, we used a three-dimensional (3D) M-to-M MIMO channel model, which takes into account the effects of fast fading and shadowing. The expressions for the considered metrics are derived as a function of the average signal-to-noise ratio per receive antenna in closed-form and are further approximated using the recursive adaptive Simpson quadrature method. Numerical results are provided to show the effects of system parameters, such as distance between antenna elements, maximum elevation angle of scatterers, orientation angle of antenna array in the x–y plane, angle between the x–y plane and the antenna array orientation, and degree of scattering in the x–y plane, on the system performance. Copyright © 2011 John Wiley & Sons, Ltd.

Analysis and compensation of power amplifier nonlinearity in MIMO transmit diversity systems

The nonlinearity of high-power amplifiers (HPAs) has a crucial effect on the performance of multiple-input-multiple-output (MIMO) systems. In this paper, we investigate the performance of MIMO orthogonal space-time block coding (OSTBC) systems in the presence of nonlinear HPAs. Specifically, we propose a constellation-based compensation method for HPA nonlinearity in the case with knowledge of the HPA parameters at the transmitter and receiver, where the constellation and decision regions of the distorted transmitted signal are derived in advance. Furthermore, in the scenario without knowledge of the HPA parameters, a sequential Monte Carlo (SMC)-based compensation method for the HPA nonlinearity is proposed, which first estimates the channel-gain matrix by means of the SMC method and then uses the SMC-based algorithm to detect the desired signal. The performance of the MIMO-OSTBC system under study is evaluated in terms of average symbol error probability (SEP), total degradation (TD) and system capacity, in uncorrelated Nakagami-m fading channels. Numerical and simulation results are provided and show the effects on performance of several system parameters, such as the parameters of the HPA model, output back-off (OBO) of nonlinear HPA, numbers of transmit and receive antennas, modulation order of quadrature amplitude modulation (QAM), and number of SMC samples. In particular, it is shown that the constellation-based compensation method can efficiently mitigate the effect of HPA nonlinearity with low complexity and that the SMC-based detection scheme is efficient to compensate for HPA nonlinearity in the case without knowledge of the HPA parameters.

Performance analysis of MIMO MRC in 3D mobile-to-mobile double-correlated channels

In this paper, we consider multiple-input multiple- output (MIMO) maximal ratio combining (MRC) systems and assess the system performance in terms of average symbol error probability (SEP), outage probability and ergodic capacity in double-correlated Rayleigh-and-Lognormal fading channels. In order to derive the receive and transmit correlation functions needed for the performance analysis, a three-dimensional (3D) MIMO mobile-to-mobile (M-to-M) channel model, which takes into account the effects of fast fading and shadowing is used. Numerical results are provided to show the effects of system parameters, such as maximum elevation angle of scatterers, orientation angle of antenna array in the x-y plane, angle between x-y plane and the antenna array orientation, and degree of scattering in the x-y plane, on the system performance.

Does neural input or processing play a greater role in the magnitude of neuroimaging signals?

An important constraint on how hemodynamic neuroimaging signals such as fMRI can be interpreted in terms of the underlying evoked activity is an understanding of neurovascular coupling mechanisms that actually generate hemodynamic responses. The predominant view at present is that the hemodynamic response is most correlated with synaptic input and subsequent neural processing rather than spiking output. It is still not clear whether input or processing is more important in the generation of hemodynamics responses. In order to investigate this we measured the hemodynamic and neural responses to electrical whisker pad stimuli in rat whisker barrel somatosensory cortex both before and after the local cortical injections of the GABAA agonist muscimol. Muscimol would not be expected to affect the thalamocortical input into the cortex but would inhibit subsequent intra-cortical processing. Pre-muscimol infusion whisker stimuli elicited the expected neural and accompanying hemodynamic responses to that reported previously. Following infusion of muscimol, although the temporal profile of neural responses to each pulse of the stimulus train was similar, the average response was reduced in magnitude by ∼79% compared to that elicited pre-infusion. The whisker-evoked hemodynamic responses were reduced by a commensurate magnitude suggesting that, although the neurovascular coupling relationships were similar for synaptic input as well as for cortical processing, the magnitude of the overall response is dominated by processing rather than from that produced from the thalamocortical input alone.

Model estimation of cerebral hemodynamics between blood flow and volume changes: A data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV.

Response of tropical cyclones to idealized climate change experiments in a global high resolution coupled general circulation model

We present an assessment of how tropical cyclone activity might change due to the influence of increased atmospheric carbon dioxide concentrations, using the UK’s High Resolution Global Environment Model (HiGEM) with N144 resolution (~90 km in the atmosphere and ~40 km in the ocean). Tropical cyclones are identified using a feature tracking algorithm applied to model output. Tropical cyclones from idealized 30-year 2×CO2 (2CO2) and 4×CO2 (4CO2) simulations are compared to those identified in a 150-year present-day simulation, which is separated into a 5-member ensemble of 30-year integrations. Tropical cyclones are shown to decrease in frequency globally by 9% in the 2CO2 and 26% in the 4CO2. Tropical cyclones only become more intese in the 4CO2, however uncoupled time slice experiments reveal an increase in intensity in the 2CO2. An investigation into the large-scale environmental conditions, known to influence tropical cyclone activity in the main development regions, is used to determine the response of tropical cyclone activity to increased atmospheric CO2. A weaker Walker circulation and a reduction in zonally averaged regions of updrafts lead to a shift in the location of tropical cyclones in the northern hemisphere. A decrease in mean ascent at 500 hPa contributes to the reduction of tropical cyclones in the 2CO2 in most basins. The larger reduction of tropical cyclones in the 4CO2 arises from further reduction of mean ascent at 500 hPa and a large enhancement of vertical wind shear, especially in the southern hemisphere, North Atlantic and North East Pacific.

Quantifying uncertainty in remotely sensed land cover maps

Remotely sensed land cover maps are increasingly used as inputs into environmental simulation models whose outputs inform decisions and policy-making. Risks associated with these decisions are dependent on model output uncertainty, which is in turn affected by the uncertainty of land cover inputs. This article presents a method of quantifying the uncertainty that results from potential mis-classification in remotely sensed land cover maps. In addition to quantifying uncertainty in the classification of individual pixels in the map, we also address the important case where land cover maps have been upscaled to a coarser grid to suit the users’ needs and are reported as proportions of land cover type. The approach is Bayesian and incorporates several layers of modelling but is straightforward to implement. First, we incorporate data in the confusion matrix derived from an independent field survey, and discuss the appropriate way to model such data. Second, we account for spatial correlation in the true land cover map, using the remotely sensed map as a prior. Third, spatial correlation in the mis-classification characteristics is induced by modelling their variance. The result is that we are able to simulate posterior means and variances for individual sites and the entire map using a simple Monte Carlo algorithm. The method is applied to the Land Cover Map 2000 for the region of England and Wales, a map used as an input into a current dynamic carbon flux model.