Wave resource variability: Impacts on wave power supply over regional to international scales

The intermittent, irregular and variable nature of the wave energy resource has implications for the supply of wave-generated electricity into the grid; intermittency of renewable power may lead to frequency and voltage fluctuations in the transmission and distribution networks. This study analyses the wave resource over different spatial scales to investigate the potential impacts of the resource variability on the grid supply. It is found that the deployment of multiple wave energy sites results in a reduction in step changes in power, leading to an overall smoothing of the wave-generated electrical power

Probabilistic solar wind and geomagnetic forecasting using an analogue ensemble or "Similar Day" approach

Effective space-weather prediction and mitigation requires accurate forecasting of near-Earth solar-wind conditions. Numerical magnetohydrodynamic models of the solar wind, driven by remote solar observations, are gaining skill at forecasting the large-scale solar-wind features that give rise to near-Earth variations over days and weeks. There remains a need for accurate short-term (hours to days) solar-wind forecasts, however. In this study we investigate the analogue ensemble (AnEn), or “similar day”, approach that was developed for atmospheric weather forecasting. The central premise of the AnEn is that past variations that are analogous or similar to current conditions can be used to provide a good estimate of future variations. By considering an ensemble of past analogues, the AnEn forecast is inherently probabilistic and provides a measure of the forecast uncertainty. We show that forecasts of solar-wind speed can be improved by considering both speed and density when determining past analogues, whereas forecasts of the out-of-ecliptic magnetic field [ BNBN ] are improved by also considering the in-ecliptic magnetic-field components. In general, the best forecasts are found by considering only the previous 6 – 12 hours of observations. Using these parameters, the AnEn provides a valuable probabilistic forecast for solar-wind speed, density, and in-ecliptic magnetic field over lead times from a few hours to around four days. For BNBN , which is central to space-weather disturbance, the AnEn only provides a valuable forecast out to around six to seven hours. As the inherent predictability of this parameter is low, this is still likely a marked improvement over other forecast methods. We also investigate the use of the AnEn in forecasting geomagnetic indices Dst and Kp. The AnEn provides a valuable probabilistic forecast of both indices out to around four days. We outline a number of future improvements to AnEn forecasts of near-Earth solar-wind and geomagnetic conditions

Volcanic Gases:Silent Killers

This is the accepted manuscript. The final version is available at http://link.springer.com/chapter/10.1007%2F11157_2015_14.Volcanic gases are insidious and often overlooked hazards. The effects of volcanic gases on life may be direct, such as asphyxiation, respiratory diseases and skin burns; or indirect, e.g. regional famine caused by the cooling that results from the presence of sulfate aerosols injected into the stratosphere during explosive eruptions. Although accounting for fewer fatalities overall than some other forms of volcanic hazards, history has shown that volcanic gases are implicated frequently in small-scale fatal events in diverse volcanic and geothermal regions. In order to mitigate risks due to volcanic gases, we must identify the challenges. The first relates to the difficulty of monitoring and hazard communication: gas concentrations may be elevated over large areas and may change rapidly with time. Developing alert and early warning systems that will be communicated in a timely fashion to the population is logistically difficult. The second challenge focuses on education and understanding risk. An effective response to warnings requires an educated population and a balanced weighing of conflicting cultural beliefs or economic interests with risk. In the case of gas hazards, this may also mean having the correct personal protection equipment, knowing where to go in case of evacuation and being aware of increased risk under certain sets of meteorological conditions. In this chapter we review several classes of gas hazard, the risks associated with them, potential risk mitigation strategies and ways of communicating risk. We discuss carbon dioxide flows and accumulations, including lake overturn events which have accounted for the greatest number of direct fatalities, the hazards arising from the injection of sulfate aerosol into the troposphere and into the stratosphere. A significant hazard facing the UK and northern Europe is a “Laki”-style eruption in Iceland, which will be associated with increased risk of respiratory illness and mortality due to poor air quality when gases and aerosols are dispersed over Europe. We discuss strategies for preparing for a future Laki style event and implications for society

Developing and testing a method to measure academic societal impact

This paper aims to extend understanding of the business and societal impact of academic research. From a business school perspective, it has taken stock of the role of academic research and relevance in business and society. The proposed conceptual framework highlights the forces influencing the pursuit of academic rigour and relevance in scholarly outputs. A theoretical model for measuring the societal impact of academic journal articles—the Academic Rigour and Relevance Index (AR2I)—was developed. This index comprises six key parameters, which are assessed by three stakeholder groups connected with academic research into business issues, these groups being: business practitioners, society and academics. The behaviour of the AR2I model was evaluated using the Monte Carlo simulation model. Taking into account the relationships between the standard deviations and the differences of classification between articles with different levels of rigour and relevance, it is demonstrated that the AR2I model is an effective tool

World productivity growth: a model averaging approach

Policy makers and the economic researchers who provide them estimates of economic activity need to have an informative and scientifically-based method to develop a consensus estimate for the most basic of the productivity measures, total factor productivity (TFP) growth. We discuss methods to combine the various estimates based on different empirical specifications that model and estimate productivity growth. We also discuss the various econometric approaches used in the profession to estimate productivity growth. Our focus is on world TFP growth

Further evidence on forecasting space weather: Frequency domain and state transition models

AbstractSpace weather reflects multiple causes. There is a clear influence for the sun on the near-earth environment. Solar activity shows evidence of chaotic properties, implying that prediction may be limited beyond short horizons. At the same time, geomagnetic activity also reflects the rotation of the earth’s core, and local currents in the ionosphere. The combination of influences means that geomagnetic indexes behave like multifractals, exhibiting nonlinear variability, with intermittent outliers. This study tests models combining frequency domain and state transition methods. Combined models are attractive in that when the data have a complex structure, a sequence of models can be used to identify individual components. The frequency domain forecast is used in the first stage. In the second stage, forecasts for solar activity are used in models for geomagnetism, and state transition terms are used to predict outliers. Forecasting tests are run for sunspots, irradiance, and two geomagnetic indexes (Aa and Am), over horizons of 1–7days. For both the solar series and the geomagnetic indexes, the most accurate forecasts are achieved by a first-stage frequency domain model and a second stage regression. The results for the more complex models are equivocal. Including the solar forecasts in the models for geomagnetic activity achieves only small gains in accuracy. The state transition models improve accuracy only over short horizons, on the order of 1day. If all the states are known perfectly, the forecast error can be reduced by as much as 4 percentage points. Unfortunately, the states are difficult to predict. Two methods are essayed, logistic regressions and neural networks. When the predicted states are included in the equations for space weather, the improvement at 1day is about 1 percentage point, but there is little improvement over longer horizons

Volcanic emissions and air pollution: Forecasts from time series models

The Kilauea volcano in Hawai'i has been erupting continuously since 1983. The eruptions are a major source of air pollution. Sulfur oxides released from the volcano react with sunlight, atmospheric gases and aerosols, and convert to fine particles. The resulting volcanic smog, known as vog, is a recognized health hazard. Data sets for volcanic emissions, sulfur dioxide gas and particulate matter are analyzed. Volcanic emissions are available at a daily resolution. Emissions show evidence of nonlinear variability, and intermittent trending over periods of several months. Forecasting tests at horizons of 1–7 days find that emissions can be predicted only with a substantial error. Air quality data is available hourly. Forecasting experiments for SO2 and particulate matter are run over horizons of 1–24 h and 1–7 days, using time series models. Volcanic emissions were tested as causal inputs in the models for the SO2 and particulate matter data, but were found to increase the forecast error. The daily and hourly SO2 series are predicted more accurately by ARIMAs. The results for particulate matter are more ambiguous. Over short horizons, they are often predicted better by regressions on levels or simple persistence models. At horizons associated with cycles (24 h, 7 days), ARIMAs and persistence forecasts achieve the most accurate results. Keywords: Volcanic emissions, Air pollution, Time series model