We describe algorithms for creating probabilistic scenarios for the situation when the underlying forecast methodology is modeled as being more (or less) accurate than it has been historically. Such scenarios can be used in studies that extend into the future and may need to consider the possibility that forecast technology will improve. Our approach can also be used to generate alternative realizations of renewable energy production that are consistent with historical forecast accuracy, in effect serving as a method for creating families of realistic alternatives -- which are often critical in simulation-based analysis methodologies
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