Admissible Evolution and Scenario Identity in Continuously Updated Forecasting Systems

Modern forecasting systems operate as continuously updating dynamical inference processes. At each update step, new observational data is assimilated and a revised forecast state is produced. These forecast states form a temporal sequence representing the system’s evolving prediction of future conditions. While substantial progress has been made in improving predictive accuracy, current forecasting architectures impose no explicit structural constraint on the admissibility of transitions between successive forecast states. As a result, forecast evolution may exhibit structural discontinuities, including abrupt changes in dominant scenario families, instability in ensemble geometry, or silent regime transitions that alter the identity of the forecast without explicit detection. This project establishes the principle of admissible forecast evolution. Under this principle, successive forecast states must preserve scenario identity continuity unless a structurally justified transition occurs. The coherence boundary defines the admissible set of forecast-state transitions that preserve structural coherence across updates. Formally, let F_t represent the forecast state at time t, and let F_{t+1} represent a candidate forecast state after assimilation of new observations. The admissibility condition requires that the transition F_t → F_{t+1} preserve structural coherence within the forecast-state manifold, except where structural transition is justified by admissible evolution of the underlying system. This framework introduces a forecast evolution governance layer independent of the predictive model itself. It does not propose a new forecasting model, but instead establishes a structural constraint governing how forecast states are permitted to evolve under continuous update. This principle applies directly to continuously updated weather forecasting systems, including hurricane forecasting and catastrophic event prediction. In such systems, successive forecast cycles may alter projected track, intensity, or impact region. The coherence boundary provides a formal mechanism for preserving continuity of scenario identity across updates and detecting structural regime stabilization or transition. This registration establishes the existence and applicability of admissible evolution constraints in continuously updated forecasting systems. It extends the broader coherence boundary framework governing identity preservation in evolving predictive and inference systems, originally developed by C. Douglas Barr, Jr., founder of Naura Systems, Inc. This registration serves as a public priority record for the application of coherence boundary principles to forecast evolution governance. This registration does not disclose operational implementation methods and does not grant license for implementation or commercial use.