Comparison of ensemble‐MOS methods in the Lorenz '96 setting

By D. Wilks. 2006

147 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/4f00c2f66ff91d682374e94e1b958a4e2ae1e5f2

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Citations in the corpus

Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging (2007, background): “…show that BMA where the EM is permitted to adjust the weights individually for each member is not an appropriate application of the technique when the sample size is small;1 specifically, the radically unequal weights of W07 exemplify an “overfitting” (Wilks 2006a, p. 207) to the training data.”
Extending Extended Logistic Regression: Extended versus Separate versus Ordered versus Censored (2014, methodology): “, with separate coefficient vectors b) are fitted for several thresholds qj of interest (e.g., Hamill et al. 2004; Wilks 2006; Wilks and Hamill 2007).”
Bias Correction and Bayesian Model Averaging for Ensemble Forecasts of Surface Wind Direction (2010, methodology): “…approach is based on regression , using model output statistics (MOS) schemes based on multiple linear regression for linear variables, such as temperature or pressure, and logistic regression for binary variables, such as precipitation occurrence or freezing (Glahn and Lowry 1972; Wilks 2006a).”
Local Proper Scoring Rules (2009, background): “Wilks (2006), Wilks and Hamill (2007) and Bröcker and Smith (2008) review various approaches to the statistical postprocessing of ensemble forecasts.”
Ensemble Streamflow Prediction: Climate signal weighting methods vs. Climate Forecast System Reanalysis (2012, methodology): “The two performance measures included the Ranked Probability Skill Score (RPSS) which is widely used in probabilistic forecast verification (Wilks, 2006) is a measure of the closeness of the cumulative probabilities of the forecast and the observation.”
Heteroscedastic Extended Logistic Regression for Postprocessing of Ensemble Guidance (2014, methodology): “Comparisons of these and other methods (Wilks 2006a; Wilks and Hamill 2007) showed that logistic regression is one of the better approaches.”
Skill of Multimodel ENSO Probability Forecasts (2008, methodology): “Recently “ensemble MOS” methods have been developed to correct the entire forecast distribution (Wilks 2006; Wilks and Hamill 2007).”
A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts (2010, methodology): “The parameters W k , c 0 , and c 1 are estimated by the maximum likelihood technique (Wilks 2006b) from the training data.”
Medium‐range multimodel ensemble combination and calibration (2009, methodology): “This approach, known as Model Output Statistics (MOS) has been successfully used in the statistical post-processing of many weather and climate forecasts (e.g. Wilks, 2006; Doblas-Reyes et al., 2005; Stephenson et al., 2005; Kharin and Zwiers, 2002).”