ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Reducing Forecast Uncertainty and Improving Forecasting Capability——A Review of the Development and Application of Modern Ensemble Forecasting Systems
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Abstract:
Abstract: This paper reviews the development history, key technologies, and application value of ensemble forecasting. Ensemble forecasting originated from the understanding of atmospheric nonlinearity and chaotic characteristics. Since Lorenz proposed the "butterfly effect" in the 1960s, it has quantified forecast uncertainty by introducing multiple perturbation experiments into numerical weather prediction. Initial perturbation techniques evolved from breeding vectors and singular vectors to ensemble Kalman filtering, while model perturbation techniques include stochastic kinetic energy backscatter, stochastic physics parameterization perturbations, and multi-physics ensembles. In the early 1990s, major international meteorological centers successively established global and regional ensemble prediction systems. Through statistical post-processing techniques, ensemble forecasting generates various probabilistic forecast products, significantly improving the accuracy and timeliness of extreme weather warnings. In recent years, AI-based ensemble models represented by Google SEEDS and et al. have achieved breakthroughs, delivering superior forecast performance at lower computational costs. Future ensemble forecasting will evolve toward a new paradigm combining physical models with AI to further enhance prediction capabilities.
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