anen

AnEn, short for Analog Ensemble, is a data-driven probabilistic forecasting method used in meteorology and related fields. It constructs forecast ensembles by exploiting historical analogue situations: when the current atmospheric state resembles past states, the observed outcomes from those past times are used to form a forecast distribution. This approach treats forecasts nonparametrically, relying on empirical evidence rather than assuming a particular statistical form.

The method builds a predictor or feature vector from current model outputs and observed conditions, such as large-scale atmospheric variables, time of year, and local weather features. A historical archive of modeled states and corresponding observations is searched for analogs—historical times with similar predictor vectors. The observed outcomes associated with these analogs form a sample of possible future states. By sampling many analogs (often with weighting by similarity), AnEn produces an ensemble forecast for each lead time. Some implementations apply rolling training windows and local adjustments to maintain relevance with changing conditions.

Applications and scope commonly include temperature, precipitation, wind, and solar irradiance, with particular emphasis in short- to mid-range forecasting. AnEn is also used in renewable energy forecasting, where accurate probabilistic forecasts of power output are valuable. Advantages include its nonparametric nature, calibration potential, and computational efficiency relative to some stochastic or fully physics-based post-processing methods. It can capture complex, non-Gaussian behaviors (such as heavy tails or skewness) that parametric models may miss.

Limitations involve the need for large, high-quality historical datasets to provide sufficient analogs, sensitivity to the choice of predictor variables and distance metrics, and potential degradation in regions or seasons with limited analogs.