Title: Quantify the Coupled Ensemble Forecast Uncertainty for the Weather and Subseasonal Prediction
Lecturer: Prof. Yuejian Zhu (China Meteorological Administration)
Time: Wednesday November 13, 2024 at 2:30 PM
Venue: Lecture Hall D102, School of Atmospheric Sciences
Abstract: Ensemble forecasting is one of the modern numerical weather prediction systems run in ensemble mode that generates multiple simulations with slightly different initial conditions and stochastic parameterizations during the model integration. The operational ensemble forecast systems were first implemented by ECMWF and NCEP in December 1992 respectively, but they used the different methods to generate initial perturbations which assumed the model was perfect. Later on the model uncertainties were introduced.
A forecast uncertainty can be quantified using a variety of methods, depending on the available information and the forecasting techniques used. Commonly, a probabilistic forecast is one of the main approaches to communicate the uncertainty associated with the ensemble forecast which helps users make informed decisions. The model uncertainty analysis is a fundamental methodology to diagnose model capability to present the forecast uncertainty in addition to ensemble forecasting skills. An optimum ensemble configuration, through adjusting the initial perturbations and model dynamic/physical perturbations (stochastic parameterization), can provide insight into forecast uncertainty.
Reference: Zhu et al, 2023: Quantify the Coupled GEFS Forecast Uncertainty for the Weather and Subseasonal Prediction. JGR Atmosphere. [Eos and JGR Atmosphere Editor Highlight]