Probabilistic forecasting from ensemble prediction systems: Improving upon the best‐member method by using a different weight and dressing kernel for each member

By Vincent Fortin, Anne-Catherine Favre, M. Saïd. 2006

92 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/b2de1dc6d0487f95df7543f7fe22192719f3b8d7

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Citations in the corpus

The continuous ranked probability score for circular variables and its application to mesoscale forecast ensemble verification (2006, background): “2006), and dressing kernels with second-moment constraints (Wang and Bishop 2005; Fortin et al. 2006).”
Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011, methodology): “…include nonhomogeneous Gaussian regression (Gneiting et al. 2005; Hagedorn et al. 2008), the best member method (Roulston and Smith 2003; Wang and Bishop 2005; Fortin et al. 2006), and logistic regression (Hamill et al. 2004), all of which have been recently compared by Wilks and Hamill (2007).”
Comparison of Ensemble-MOS Methods Using GFS Reforecasts (2007, background): “For example, Fortin et al. (2006) have recently proposed an ensemble-dressing method in which different members of the ranked en- semble may use different dressing kernels.”
Verification and Calibration of Neighborhood and Object-Based Probabilistic Precipitation Forecasts from a Multimodel Convection-Allowing Ensemble (2012, background): “…(Kass and Raftery 1995; Raftery et al. 2005; Sloughter et al. 2007), Gaussian ensemble dressing (GED; Roulston and Smith 2003; Wang and Bishop 2005; Fortin et al. 2006), logistic regression (LR; Hamill et al. 2004, 2008), analog forecasts (Zhu and Toth 2000; Hamill and Whitaker 2006), artificial…”
Performance and reliability of multimodel hydrological ensemble simulations based on seventeen lumped models and a thousand catchments (2010, unknown): “Statistical procedures exist to calibrate unreliable probabilistic simulations (e.g., Raftery et al., 2005; Fortin et al., 2006; Stensrud and Yussouf, 2007).”