The move from copper to fiber optics is a very natural transition forecasted by just about every industry pundit.
Previous research indicated that in order to attain an acceptable sampling error, the ensemble size must be on the order of hundreds (for probabilistic river stage forecasts and probabilistic flood forecasts) or even thousands (for probabilistic stage transition forecasts).
However, as we will show in the following section, several elements as well as their dynamics in the technological field currently remain nebulous, thereby making it difficult to assess or even forecast transition processes.
Turkey is now making the transition that I forecast years ago from a secondary power to a major regional power.
Although both approaches combine the same mortality trends in their forecasts, the transition between observed and forecasted values appears to be more plausible (or smooth) for our model than for the coherent model; at least we were unable to remove this jump-off bias using the web-based platform LCFIT.
Further, it is critical to understand the time of occurrence of earthquakes, the rupture patterns, and nucleation processes, and to forecast slip transition in the future.
For the post-failure stage, an enhanced numerical approach is provided to understand and forecast the transition from a slide to a flow.
We assume that such a transition to warn-on-forecast will not only change the role of the forecaster (Stuart et al. 2006), but will also affect the practices of forecast end-users and responders to a warning.
In the context of forecasting, population transition probabilities that have been estimated for past years (eg 1992 to 1994) are extrapolated into future years for which forecasts are required (eg 1995 to 1999).
Recently, linkages have formed between futures studies and a growing research field on sustainable transitions (see Technology Forecasting and Social Change 2012, 79/6, Special Section).
