Improvement of ensemble reliability with a new dressing kernel

By Xuguang Wang, C. Bishop. 2005

113 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/aabcf6127a1d528a6e65f063043024771b7aa2fe

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Citations in the corpus

Verification and Calibration of Neighborhood and Object-Based Probabilistic Precipitation Forecasts from a Multimodel Convection-Allowing Ensemble (2012, methodology): “…are determined by a one-tailed, paired Wilcoxon signed rank test (Wilks 2006) of the daily Brier scores (Hamill 1999).4 We follow Hamill (1999) to use each day as one sample for significance testing to avoid the challenges of extracting independent samples from a single day (Wang and Bishop 2005).”
Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments (2010, unknown): “, kernel density) approaches (Roulston and Smith, 2003; Wang and Bishop, 2005; Fortin et al., 2006), Bayesian model averaging (Raftery et al.”
Comparison of ensemble‐MOS methods in the Lorenz '96 setting (2006, methodology): “For situations in which Gaussian dressing kernels are appropriate, Wang & Bishop (2005) have proposed an alternative method for defining the Gaussian dressing parameters, which they call second-moment constraint dressing.”
Probabilistic forecasting from ensemble prediction systems: Improving upon the best‐member method by using a different weight and dressing kernel for each member (2006, background): “Wang and Bishop (2005), hereafter WB05, have however shown by stochastic simulations that the best-member method can lead both to underdispersive and overdispersive ensembles.”
A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts (2010, methodology): “…of statistical methods, such as logistic regression (LR; Brelsford and Jones 1967; Wilks 2006b), Bayesian model averaging (BMA; Raftery et al. 2005), nonho-mogeneous Gaussian regression (NGR; Gneiting et al. 2005), and Gaussian ensemble dressing (GED; Roulston and Smith 2003; Wang and Bishop 2005).”
The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification (2006, background): “Several potential techniques have emerged toward meeting this challenge, including Bayesian model averaging (Raftery et al. 2005), two-step analogues from ensemble re-forecasts (Hamill et al. 2005), and dressing kernels with second moment constraints (Wang and Bishop 2005).”