Hindcasting

Hindcasting, also known as retrodiction in some disciplines, is a model validation approach in which a model is run with inputs corresponding to a past period and its outputs are compared with known historical observations. The goal is to assess how well the model would have predicted events that actually occurred, thereby evaluating predictive skill, robustness, and potential biases.

Processively, hindcasting begins with selecting a historical window where reliable observations exist. The model is run using the conditions and forcing data that would have been available at that time, often including initial conditions and boundary inputs. The generated past predictions are then compared to the actual record of what happened. Researchers use statistical measures such as root-mean-square error, correlation, bias, or probabilistic scores to quantify forecast skill and identify systematic errors.

Applications of hindcasting span many fields. In meteorology and climate science, hindcasts test weather and climate model performance for past seasons or events. In hydrology and oceanography, hindcasting assesses rainfall–runoff models or ocean circulation predictions. In ecology and economics, hindcasting helps evaluate models of population dynamics or market behavior by replaying historical scenarios.

Limitations include data quality and completeness of historical records, nonstationarity of systems, and the potential impact of data assimilation choices or model calibration on retrospective results. Hindcasting does not guarantee future accuracy, but it provides a diagnostic framework for understanding a model’s predictive capabilities and guiding improvements. It is distinct from backcasting, a planning method for defining future goals, and from backtesting in finance, which evaluates trading strategies on historical financial data.