Prescient Weather (United states)

Quality of service and fiscal success in the energy industry often depend on how well meteorological information and forecasts are used to manage risk and opportunity. On the subseasonal to seasonal (S2S) timescales, a disciplined strategy allows decision makers to counteract predicted adverse climate variations in the coming weeks or months with action or financial hedges. Calibrated S2S probabilistic forecasts from some providers have sufficient skill that they engender confidence in the statistical consequences of acting. On the scale of several or more decades ahead, probabilistic outlooks can guide strategic planning and capital expenditures in directions that will ensure long-term resilience to climate change. In both cases, the probabilities are generated by statistical analysis of ensembles of supercomputer forecasts or climate change scenarios.

Probability methods provide quantitative insight into the implications of atmospheric variability for the energy industry. Contemporary computer probability forecasts of climate anomalies for the weeks, months, or seasons ahead offer new precision in managing both risk and opportunity. The forecasts of two major international centers and a multi-model constructed from them by the World Climate Service demonstrate that the contemporary probability forecasts have sufficient skill and reliability to provide advantageous guidance for energy decisions. An analytical model of choices available in response to predicted anomalies illustrates how and when to act on forecasts. Atmospheric informatics is introduced as a system for creating, transferring, and applying atmospheric information in important endeavors. The aim is to show energy and other industry decision-makers what they need to know—now.

Some of the key interactions between the atmosphere and the energy industry are considered, with a special focus on concepts and forecasts through which atmospheric science assists the industry and its support for society. Probabilistic weather and seasonal forecasts take advantage of contemporary understanding and computer power to clarify the envelope of expectation for risk and opportunity in the industry on the scale of days and seasons. The quality of seasonal forecasts is examined with two statistical verification schemes. Climate change, regardless of cause, poses potential challenges for the energy industry, perhaps the most significant arising from political and economic pressures. A formal analysis scheme is outlined that may assist energy companies to foresee and manage the implications and consequences of climate change for their business.