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Application of a Bayesian weighting for short‐range lagged ensemble forecasting at the convective scale

Ensemble prediction systems at the convective scale are often under‐dispersive. In order to alleviate this problem, a time‐lagged ensemble can be created from ensemble forecasts initialized at different production times. While an equal‐weight combination of lagged forecasts generally provides competitive results, this article introduces and discusses the efficiency of an objective weighting. The proposed approach is based on nonlinear Bayesian filtering, and the weights are determined online for each member according to the observation likelihood.