GeCC-LAG-ENSEMBLES: a Generalized Calibration and Combination approach to mix in an optimum way lagged multi-model ensemble forecasts

• Applicant: Professor Paolo Reggiani, Ph.D.
• Subject Area: Atmospheric Science
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 490941584

In this proposal we aim to develop a Generalized Calibration and Combination (GeCC) methodology and apply it to combine in an optimum way lagged ensembles generated by different weather forecasting models. The generalized combination technique involves weights that are determined on the basis of two Bayesian methods: Bayesian Model Averaging (BMA) and the correlation-based Model Conditional Processor (MCP) method. The GeCC-processed probabilistic forecasts can be expressed in terms of predictive statistics for meteorological variables over the spatial model grid, or at station locations. The GeCC technique could be used to merge ensembles with different characteristics to generate more accurate and reliable probabilistic products, than the ones that could be generated by a single ensemble is available. These ensembles could be, for example, lagged global medium-range, sub-seasonal and seasonal ensembles (e.g. produced by the European Center for Medium-Range Weather Forecasts), or lagged global and limited-area ensembles (e.g. produced by the Deutscher Wetterdienst). In particular, GeCC-processed probabilistic forecasts of rare events are expected to improve.The GeCC technique could also lead the way toward smarter and computationally more efficient ways of computing combination weights, and/or calibration coefficients that today are computed using re-forecast datasets that currently absorb considerable computational resources to be generated. The three main outcomes of this project are going to be: - A pre-operational weighting procedure that allows to combine multiple lagged ensemble forecasts with different forecasting horizons. - A systematic verification of the proposed method through error statistics, including meteorological extremes, for selected variables and locations in at least one geographical region in Europe. - Estimation of a calibrated predictive distributions for selected variables at selected forecast locations and/or for the selected geographical region.

• DFG Programme: Research Grants
• International Connection: Italy
• Cooperation Partner: Professor Roberto Buizza, Ph.D.