Improvement of the Multimodel Superensemble Technique for Seasonal Forecasts

By W. Yun, L. Stefanova, T. N. Krishnamurti. 2003

145 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/4895ab6a986ab01ea1bffabfd282b3b828302ff5

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Citations in the corpus

Multimodel Simulation of Water Flow in a Field Soil Using Pedotransfer Functions (2009, methodology): “Yun et al. (2003) showed that improvements in superensemble forecasts can be achieved by applying an SVD technique to solve the system of equations for the coeffi cients wi. Bayesian Model Averaging Following Raftery et al. (2003), a basic premise is the assumption that for any given forecast,…”
A Bayesian Framework for Multimodel Regression (2007, methodology): “This solution is a “smoother” version of the truncation method of Yun et al. (2003), in which all but the largest singular values are “zeroed out.”
Development of New Ensemble Methods Based on the Performance Skills of Regional Climate Models over South Korea (2012, methodology): “Since Krishnamurti et al. (1999, 2000) and Yun et al. (2003) showed that the multimodel ensemble (MME) is superior to the single model by using an ensemble of global climate models from the Atmospheric Model Intercomparison Project (AMIP), various types of MMEs have been developed and widely…”
Spatial Analysis to Quantify Numerical Model Bias and Dependence (2008, background): “Examples where this has been confirmed are seasonal weather prediction (Yun, Stefanova, and Krishnamurti 2003), detection and attribution (Gillett et al. 2002), health (Thomson et al. 2006), and agriculture (Cantelaube and Terres 2005).”
Skill of Multimodel ENSO Probability Forecasts (2008, methodology): “In the case of Gaussian regression, the number of predictors can be reduced, and hence the number of parameters to be estimated, using canonical correlation analysis (CCA) or singular value decomposition (SVD; Yun et al. 2003).”
The use of the multi-model ensemble in probabilistic climate projections (2007, background): “Seasonal forecasts show better skill, higher reliability and consistency when several independent models are combined (e.g. Doblas-Reyes et al. 2003; Yun et al. 2003).”
Challenges in Combining Projections from Multiple Climate Models (2010, background): “Weather and seasonal forecasts show improved skill, higher reliability, and consistency when multiple models are combined (Krishnamurti et al. 1999; DoblasReyes et al. 2003; Yun et al. 2003).”
Assessment of CMIP5 climate models and projected temperature changes over Northern Eurasia (2014, methodology): “In general, the approach where model weights are determined by the model skill performs better than the method of equally weighing all models (e.g., Yun et al 2003, Tebaldi and Knutti 2007, Ra ̈isa ̈nen and Ylha ̈isi 2012).”
Bayesian estimation of local signal and noise in multimodel simulations of climate change (2010, methodology): “[8] The empirical benefits of multimodel averaging for enhancing the skill of weather and historical climate predictions have motivated the development of more sophisticated averaging approaches that optimize the statistical weightings according to the agreement of model predictions with observation”
A review of multimodel superensemble forecasting for weather, seasonal climate, and hurricanes (2016, methodology): “For climate forecasts, a slightly modified superensemble method is constructed, which is called the synthetic superensemble [Yun et al., 2003; Krishnamurti and Kumar, 2012; Kumar and Krishnamurti, 2012].”