Blowing in the solar wind

When the solar wind blows: The northern lights are a sign of the awesome power that the Earth receives from the solar wind. The big puzzle is how

The importance of accurate wind resource assessment for evaluating the economic viability of small wind turbines

The techno-economic performance of a small wind turbine is very sensitive to the available wind resource. However, due to financial and practical constraints installers rely on low resolution wind speed databases to assess a potential site. This study investigates whether the two site assessment tools currently used in the UK, NOABL or the Energy Saving Trust wind speed estimator, are accurate enough to estimate the techno-economic performance of a small wind turbine. Both the tools tend to overestimate the wind speed, with a mean error of 23% and 18% for the NOABL and Energy Saving Trust tool respectively. A techno-economic assessment of 33 small wind turbines at each site has shown that these errors can have a significant impact on the estimated load factor of an installation. Consequently, site/turbine combinations which are not economically viable can be predicted to be viable. Furthermore, both models tend to underestimate the wind resource at relatively high wind speed sites, this can lead to missed opportunities as economically viable turbine/site combinations are predicted to be non-viable. These results show that a better understanding of the local wind resource is a required to make small wind turbines a viable technology in the UK.

A novel combined biomarker including plasma carotenoids, vitamin C, and ferric reducing antioxidant power is more strongly associated with fruit and vegetable intake than the individual components

BACKGROUND: Monitoring of fruit and vegetable (F&V) intake is fraught with difficulties. Available dietary assessment methods are associated with considerable error, and the use of biomarkers offers an attractive alternative. Few studies to date have examined the use of plasma biomarkers to monitor or predict the F&V intake of volunteers consuming a wide range of intakes from both habitual F&V and manipulated diets. OBJECTIVE: This study tested the hypothesis that an integrated biomarker calculated from a combination of plasma vitamin C, cholesterol-adjusted carotenoid concentration and Ferric Reducing Antioxidant Power (FRAP) had more power to predict F&V intake than each individual biomarker. METHODS: Data from a randomized controlled dietary intervention study [FLAVURS (Flavonoids University of Reading Study); n = 154] in which the test groups observed sequential increases of 2.3, 3.2, and 4.2 portions of F&Vs every 6 wk across an 18-wk period were used in this study. RESULTS: An integrated plasma biomarker was devised that included plasma vitamin C, total cholesterol-adjusted carotenoids, and FRAP values, which better correlated with F&V intake (r = 0.47, P < 0.001) than the individual biomarkers (r = 0.33, P < 0.01; r = 0.37, P < 0.001; and r = 0.14, respectively; P = 0.099). Inclusion of urinary potassium concentration did not significantly improve the correlation. The integrated plasma biomarker predicted F&V intake more accurately than did plasma total cholesterol-adjusted carotenoid concentration, with the difference being significant at visit 2 (P < 0.001) and with a tendency to be significant at visit 1 (P = 0.07). CONCLUSION: Either plasma total cholesterol-adjusted carotenoid concentration or the integrated biomarker could be used to distinguish between high- and moderate-F&V consumers. This trial was registered at www.controlled-trials.com as ISRCTN47748735.

The error of representation: basic understanding

Representation error arises from the inability of the forecast model to accurately simulate the climatology of the truth. We present a rigorous framework for understanding this kind of error of representation. This framework shows that the lack of an inverse in the relationship between the true climatology (true attractor) and the forecast climatology (forecast attractor) leads to the error of representation. A new gain matrix for the data assimilation problem is derived that illustrates the proper approaches one may take to perform Bayesian data assimilation when the observations are of states on one attractor but the forecast model resides on another. This new data assimilation algorithm is the optimal scheme for the situation where the distributions on the true attractor and the forecast attractors are separately Gaussian and there exists a linear map between them. The results of this theory are illustrated in a simple Gaussian multivariate model.

Challenges in quantifying wind generation's contribution to securing peak demand

Wind generation's contribution to supporting peak electricity demand is one of the key questions in wind integration studies. Differently from conventional units, the available outputs of different wind farms cannot be approximated as being statistically independent, and hence near-zero wind output is possible across an entire power system. This paper will review the risk model structures currently used to assess wind's capacity value, along with discussion of the resulting data requirements. A central theme is the benefits from performing statistical estimation of the joint distribution for demand and available wind capacity, focusing attention on uncertainties due to limited histories of wind and demand data; examination of Great Britain data from the last 25 years shows that the data requirements are greater than generally thought. A discussion is therefore presented into how analysis of the types of weather system which have historically driven extreme electricity demands can help to deliver robust insights into wind's contribution to supporting demand, even in the face of such data limitations. The role of the form of the probability distribution for available conventional capacity in driving wind capacity credit results is also discussed.

Atmospheric and oceanic contributions to irreducible forecast uncertainty of Arctic surface climate

Uncertainty of Arctic seasonal to interannual predictions arising from model errors and initial state uncertainty has been widely discussed in the literature, whereas the irreducible forecast uncertainty (IFU) arising from the chaoticity of the climate system has received less attention. However, IFU provides important insights into the mechanisms through which predictability is lost, and hence can inform prioritization of model development and observations deployment. Here, we characterize how internal oceanic and surface atmospheric heat fluxes contribute to IFU of Arctic sea ice and upper ocean heat content in an Earth system model by analyzing a set of idealized ensemble prediction experiments. We find that atmospheric and oceanic heat flux are often equally important for driving unpredictable Arctic-wide changes in sea ice and surface water temperatures, and hence contribute equally to IFU. Atmospheric surface heat flux tends to dominate Arctic-wide changes for lead times of up to a year, whereas oceanic heat flux tends to dominate regionally and on interannual time scales. There is in general a strong negative covariance between surface heat flux and ocean vertical heat flux at depth, and anomalies of lateral ocean heat transport are wind-driven, which suggests that the unpredictable oceanic heat flux variability is mainly forced by the atmosphere. These results are qualitatively robust across different initial states, but substantial variations in the amplitude of IFU exist. We conclude that both atmospheric variability and the initial state of the upper ocean are key ingredients for predictions of Arctic surface climate on seasonal to interannual time scales.

The prediction of Northern Hemisphere tropical cyclone extended life cycles by the ECMWF Ensemble and Deterministic Prediction Systems. Part I: tropical cyclone stage

This study has explored the prediction errors of tropical cyclones (TCs) in the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) for the Northern Hemisphere summer period for five recent years. Results for the EPS are contrasted with those for the higher-resolution deterministic forecasts. Various metrics of location and intensity errors are considered and contrasted for verification based on IBTrACS and the numerical weather prediction (NWP) analysis (NWPa). Motivated by the aim of exploring extended TC life cycles, location and intensity measures are introduced based on lower-tropospheric vorticity, which is contrasted with traditional verification metrics. Results show that location errors are almost identical when verified against IBTrACS or the NWPa. However, intensity in the form of the mean sea level pressure (MSLP) minima and 10-m wind speed maxima is significantly underpredicted relative to IBTrACS. Using the NWPa for verification results in much better consistency between the different intensity error metrics and indicates that the lower-tropospheric vorticity provides a good indication of vortex strength, with error results showing similar relationships to those based on MSLP and 10-m wind speeds for the different forecast types. The interannual variation in forecast errors are discussed in relation to changes in the forecast and NWPa system and variations in forecast errors between different ocean basins are discussed in terms of the propagation characteristics of the TCs.

Increasing thermal plant flexibility in a high renewables power system

Thermal generation is a vital component of mature and reliable electricity markets. As the share of renewable electricity in such markets grows, so too do the challenges associated with its variability. Proposed solutions to these challenges typically focus on alternatives to primary generation, such as energy storage, demand side management, or increased interconnection. Less attention is given to the demands placed on conventional thermal generation or its potential for increased flexibility. However, for the foreseeable future, conventional plants will have to operate alongside new renewables and have an essential role in accommodating increasing supply-side variability. This paper explores the role that conventional generation has to play in managing variability through the sub-system case study of Northern Ireland, identifying the significance of specific plant characteristics for reliable system operation. Particular attention is given to the challenges of wind ramping and the need to avoid excessive wind curtailment. Potential for conflict is identified with the role for conventional plant in addressing these two challenges. Market specific strategies for using the existing fleet of generation to reduce the impact of renewable resource variability are proposed, and wider lessons from the approach taken are identified.

Decadal predictability of regional scale wind speed and wind energy potentials over Central Europe

Decadal predictions on timescales from one year to one decade are gaining importance since this time frame falls within the planning horizon of politics, economy and society. The present study examines the decadal predictability of regional wind speed and wind energy potentials in three generations of the MiKlip (‘Mittelfristige Klimaprognosen’) decadal prediction system. The system is based on the global Max-Planck-Institute Earth System Model (MPI-ESM), and the three generations differ primarily in the ocean initialisation. Ensembles of uninitialised historical and yearly initialised hindcast experiments are used to assess the forecast skill for 10 m wind speeds and wind energy output (Eout) over Central Europe with lead times from one year to one decade. With this aim, a statistical-dynamical downscaling (SDD) approach is used for the regionalisation. Its added value is evaluated by comparison of skill scores for MPI-ESM large-scale wind speeds and SDD-simulated regional wind speeds. All three MPI-ESM ensemble generations show some forecast skill for annual mean wind speed and Eout over Central Europe on yearly and multi-yearly time scales. This forecast skill is mostly limited to the first years after initialisation. Differences between the three ensemble generations are generally small. The regionalisation preserves and sometimes increases the forecast skills of the global runs but results depend on lead time and ensemble generation. Moreover, regionalisation often improves the ensemble spread. Seasonal Eout skills are generally lower than for annual means. Skill scores are lowest during summer and persist longest in autumn. A large-scale westerly weather type with strong pressure gradients over Central Europe is identified as potential source of the skill for wind energy potentials, showing a similar forecast skill and a high correlation with Eout anomalies. These results are promising towards the establishment of a decadal prediction system for wind energy applications over Central Europe.

An idealized study of coupled atmosphere-ocean 4D-Var in the presence of model error

Atmosphere only and ocean only variational data assimilation (DA) schemes are able to use window lengths that are optimal for the error growth rate, non-linearity and observation density of the respective systems. Typical window lengths are 6-12 hours for the atmosphere and 2-10 days for the ocean. However, in the implementation of coupled DA schemes it has been necessary to match the window length of the ocean to that of the atmosphere, which may potentially sacrifice the accuracy of the ocean analysis in order to provide a more balanced coupled state. This paper investigates how extending the window length in the presence of model error affects both the analysis of the coupled state and the initialized forecast when using coupled DA with differing degrees of coupling. Results are illustrated using an idealized single column model of the coupled atmosphere-ocean system. It is found that the analysis error from an uncoupled DA scheme can be smaller than that from a coupled analysis at the initial time, due to faster error growth in the coupled system. However, this does not necessarily lead to a more accurate forecast due to imbalances in the coupled state. Instead coupled DA is more able to update the initial state to reduce the impact of the model error on the accuracy of the forecast. The effect of model error is potentially most detrimental in the weakly coupled formulation due to the inconsistency between the coupled model used in the outer loop and uncoupled models used in the inner loop.

Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game

Probabilistic hydro-meteorological forecasts have over the last decades been used more frequently to communicate forecastuncertainty. This uncertainty is twofold, as it constitutes both an added value and a challenge for the forecaster and the user of the forecasts. Many authors have demonstrated the added (economic) value of probabilistic over deterministic forecasts across the water sector (e.g. flood protection, hydroelectric power management and navigation). However, the richness of the information is also a source of challenges for operational uses, due partially to the difficulty to transform the probability of occurrence of an event into a binary decision. This paper presents the results of a risk-based decision-making game on the topic of flood protection mitigation, called “How much are you prepared to pay for a forecast?”. The game was played at several workshops in 2015, which were attended by operational forecasters and academics working in the field of hydrometeorology. The aim of this game was to better understand the role of probabilistic forecasts in decision-making processes and their perceived value by decision-makers. Based on the participants’ willingness-to-pay for a forecast, the results of the game show that the value (or the usefulness) of a forecast depends on several factors, including the way users perceive the quality of their forecasts and link it to the perception of their own performances as decision-makers.

Wind farm construction impacts reindeer migration and movement corridors

Over the last decade, we have seen a massive increase in the construction of wind farms in northern Fennoscandia. Wind farms comprising hundreds of wind turbines are being built, with little knowledge of the possible cumulative adverse effects on the habitat use and migration of semi-domesticated free-ranging reindeer. We assessed how reindeer responded to wind farm construction in an already fragmented landscape, with specific reference to the effects on use of movement corridors and reindeer habitat selection. We used GPS-data from reindeer during calving and post-calving in the MalAyen reindeer herding community in Sweden. We analysed data from the pre-development years compared to the construction years of two relatively small wind farms. During construction of the wind farms, use of original migration routes and movement corridors within 2 km of development declined by 76 %. This decline in use corresponded to an increase in activity of the reindeer measured by increased step lengths within 0-5 km. The step length was highest nearest the development and declining with distance, as animals moved towards migration corridors and turned around or were observed in holding patterns while not crossing. During construction, reindeer avoided the wind farms at both regional and landscape scale of selection. The combined construction activities associated with even a few wind turbines combined with power lines and roads in or close to central movement corridors caused a reduction in the use of such corridors and grazing habitat and increased the fragmentation of the reindeer calving ranges.

Improving the precision of the accumulated oxygen deficit using VO2-power regression points from below and above the lactate threshold

The accumulated oxygen deficit (AOD) method assumes a linear VO₂-power relationship for exercise intensities increasing from below the lactate threshold (BLT) to above the lactate threshold (ALT). Factors that were likely to effect the linearity of the VO₂-power regression and the precision of the estimated total energy demand (ETED) were investigated. These included the slow component of VO₂ kinetics (SC), a forced resting y-intercept and exercise intensities BLT and ALT. Criteria for linearity and precision included the Pearson correlation coefficient (PCC) of the VO₂-power relationship, the length of the 95% confidence interval (95% CI) of the ETED and the standard error of the predicted value (SEP), respectively. Eight trained male and one trained female triathlete completed the required cycling tests to establish the AOD when pedalling at 80 rev/min. The influence of the SC on the linear extrapolation of the ETED was reduced by measuring VO₂ after three min of exercise. Measuring VO₂ at this time provided a new linear extrapolation method consisting of ten regression points spread evenly from BLT and ALT. This method produced an ETED with increased precision compared to using regression equations developed from intensities BLT with no forced y-intercept value; (95%CI (L), 0.70±0.26 versus 1.85±1.10, P<0.01; SEP(L/Watt), 0.07±0.02 versus 0.28±0.17; P<0.01). Including a forced y-intercept value with five regression points either BLT or ALT increased the precision of estimating the total energy demand to the same level as when using 10 regression points, (5 points BLT + y-intercept versus 5 points ALT + y-intercept versus 10 points; 95%CI(l), 0.61±0.32, 0.87±0.40, 0.70±0.26; SEP(L/Watt), 0.07±0.03, 0.08±0.04, 0.07±0.02; p>0.05). The VO₂-power regression can be designed using a reduced number of regression points... ABSTRACT FROM AUTHOR

Distribution of power requirements during yarn winding in ring spinning

A model of a yam package is established for a ring spinning system. The yarn layer, surface area, and mass of the yam package are formulated with respect to the diameters of the empty bobbin and full yarn package, yarn count, and yarn winding-on time. Based on the principles of dynamics and aerodynamics, models of the power requirements for overcoming the skin friction drag, increasing the kinetic energy of the yarn package (bobbin and wound yarn), and overcoming the yarn wind-on tension are developed. The skin friction coefficient on the surface of a rotating yam package is obtained from experiment. The power distribution during yam packaging is discussed based on a case study. The results indicate that overcoming the skin friction drag during yarn winding consumes the largest amount of energy. The energy required to overcome the yarn wind-on tension is also significant.

Power analysis and simulation of a vehicle under combined loads

This paper presents an analytical model of fuel consumption (AMFC) to coordinate the driving power and manage the overall fuel consumption for an internal combustion engine vehicle. The model calculates the different loads applied on the vehicle including road-slope, road-friction, wind-drag, accessories, and mechanical losses. Also, it solves the combustion equation of the engine under different working conditions including various fuel compositions, excess airs and air inlet temperatures. Then it determines the contribution of each load to signify the energy distribution and power flows of the vehicle. Unlike the conventional models in which the vehicle speed needs to be given as an input, the developed model can predict the vehicle speed and acceleration under different working conditions by allowing the speed to vary within a predefined range only. Furthermore, the model indicates the ways to minimises the vehicles' fuel consumption under various driving conditions. The results show that the model has the potential to assist in the vehicle energy management.