Day-to-day volatility in natural gas markets is driven largely by variability in heating demand, which is in turn dominated by cool-season temperature anomalies over the northeastern quadrant of the United States (“Midwest–East”). Energy traders rely on temperature forecasts at horizons of 2–4 weeks to anticipate those fluctuations in demand. Forecasts from dynamical models are widely available, so the markets react quickly to changes in the model predictions. Traders often work with meteorologists who leverage teleconnections from the tropics and the Arctic to improve upon the model forecasts. This study demonstrates how natural gas prices react to Midwest–East temperatures using the anomalous winters of 2011/12 and 2013/14. These examples also illustrate how energy meteorologists use teleconnections from the Arctic and the tropics to forecast heating demand. Winter 2011/12 was exceptionally warm, consistent with the positive Arctic Oscillation (AO). March 2012 was a fitting exclamation point on the winter as it featured the largest warm anomaly for the United States above the twentieth-century climatology of any month since 1895. The resulting lack of heating demand led to record surpluses of natural gas storage and spurred prices downward to an 11-yr low in April 2012. In sharp contrast, winter 2013/14 was unusually cold. An anomalous Alaskan ridge led to cold air being transported from Siberia into the United States, despite the AO generally being positive. The ensuing swell in heating demand exhausted the surplus natural gas inventory, and prices rose to their highest levels since the beginning of the global recession in 2008.

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One of the most difficult tasks in the structural control industry is providing linear, predictable, passive damping over a wide frequency range. This challenge has been worked around successfully in the past, but rarely has it been performed ideally. The subject matter of this paper takes a radical step toward attaining the goal of linear damping performance, while adding very low static stiffness to the system being damped.

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As the operational requirements placed on airborne surveillance systems increases many in-service systems are approaching the limits of their performance. This problem is compounded by modifications to the systems, such as the change of displays and addition of recording equipment which causes the systems not to be ergonomically and psychophysiologically optimised. Simulation has been conducted and sensor system modelling performed to assess the improvement in operational performance that is attainable by the application of image processing techniques viable for implementation in the real-time airborne environment. Equipments have been developed and operated in-service from which operational reports have confirmed the results ofthe system modelling. Future upgrades ofthe processing system will enable the application of such processing to a range of in-service systems as an intermediate, low cost system upgrade.