Implementation of realization generation from a multi-model probabilistic blended forecast

Users of gridded weather forecasts, whether derived from ensemble or deterministic sources, often require realistic weather scenarios or “realizations” that represent the range of possible forecast outcomes. This need is especially acute for hydrological applications, where precipitation realizations are essential inputs to hydrological models that subsequently generate deterministic or ensemble river flow forecasts. However, weather forecasts are available from many sources, spanning a range of spatial scales, resolutions, and lead times—from high‑resolution, short‑range forecasts covering small domains to coarse, long‑range global forecasts. Combining forecasts across timescales whilst minimising the appearance of undesirable artefacts is a continued challenge.

The IMPROVER project addresses this by converting each forecast source into exceedance probabilities and blending them into a seamless multi‑model probabilistic forecast. This blending step produces smooth, coherent probability forecasts across the full range of lead times and serves as the foundation for generating multi‑model blended realization forecasts.

The generation of these realizations follows a multi‑stage Ensemble Copula Coupling (ECC) procedure designed to preserve spatial and temporal structure, where possible. A subset of raw ensemble members is first selected using clustering to act as templates. These templates are then enhanced through temporal interpolation and the injection of stochastic noise to make them suitable for generating precipitation fields. Probability forecasts are sampled to reflect the spatial characteristics of the raw ensemble members, and an additional step ensures that intensity peaks in the generated realizations remain consistent with those present in the underlying members.  

This study presents the implementation of this blended‑realization approach and its application to driving a national‑scale hydrological model covering England and Wales. We discuss the performance of the generated realizations and highlight the benefits of this probabilistic, multi‑model strategy for operational hydrological forecasting.