Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging

By J. M. L. Sloughter, Adrian E. Raftery, Tilmann Gneiting, Chris Fraley. 2007

434 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/6b4226a7da0a9dd2070a6692f0addf00602e7e6c

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Citations in the corpus

Local Proper Scoring Rules (2009, methodology): “Arguably, the two most prevalent methods are the Bayesian model averaging (BMA) approach developed by Raftery et al. (2005) and Sloughter et al. (2007) , and the heterogeneous regression, or ensemble model output statistics (EMOS), technique of Gneiting et al. (2005).”
Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation (2008, methodology): “There are many other possible precipitation forecast calibration methodologies; see Hamill and Whitaker (2006) for a comparison of logistic regression with analog techniques using GFS reforecasts or Sloughter et al. (2007) for a Bayesian model averaging approach.”
Verification and Calibration of Neighborhood and Object-Based Probabilistic Precipitation Forecasts from a Multimodel Convection-Allowing Ensemble (2012, methodology): “…relationships (Atger 1999), fitted distribution parameters (Wilks 2002), Bayesian model averaging (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…”
A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts (2010, methodology): “Apart from the precipitation threshold and its square root (as in Wilks 2009), the square root and cube root (Sloughter et al. 2007) of the ensemble mean total precipitation (TP) are used as potential predictors, as well as the ensemble mean of the square root of TP.”
Local proper scoring rules of order two (2011, methodology): “State-of-the-art methods include the Bayesian model averaging (BMA) approach developed by Raftery et al. (2005) and Sloughter et al. (2007), Sloughter, Gneiting and Raftery (2010), and the heterogeneous regression, or ensemble model output statistics (EMOS), technique of Gneiting et al. (2005) and…”
Performance of Optimally Merged Multisatellite Precipitation Products Using the Dynamic Bayesian Model Averaging Scheme Over the Tibetan Plateau (2017, methodology): “The BMA data received higher relative weight from better performing member because the likelihood of an individual was essentially a measure of the agreement between satellite precipitation estimates and gaugebased observations (Duan et al., 2007; Sloughter et al., 2007).”
Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE (2012, methodology): “Wilks (2009) used the square root, Sloughter et al. (2007) the third root, and Hamill et al. (2004, 2008) the fourth root of precipitation.”
Postprocessing of Ensemble Precipitation Predictions with Extended Logistic Regression Based on Hindcasts (2012, neutral): “Other examples range from the correction using the rank histogram (Hamill and Colucci 1998), methods based on analogs (Hamill and Whitaker 2006), Bayesian techniques (e.g., Reggiani and Weerts 2008), and Bayesian model averaging (e.g., Sloughter et al. 2007; Schmeits and Kok 2010).”
Evaluating raw ensembles with the continuous ranked probability score (2012, background): “The literature abounds in examples of scoring rules being applied to evaluate probability forecasts: Roulston and Smith (2002, 2003); Roulston et al. (2003); Gneiting et al. (2005); Raftery et al. (2005); Roulston et al. (2005); Grimit et al. (2006); Sloughter et al. (2007).”
Combining probability forecasts (2010, background): “Despite their ubiquity, these issues have frequently been overlooked, with some of our own work (Sloughter et al., 2007) being one such example.”
Probabilistic quantitative precipitation forecasting using Ensemble Model Output Statistics (2013, methodology): “Finding a suitable probabilistic model is more challenging for precipitation than for most other weather variables because the associated uncertainty calls for rather special distributions with the following features: • they must be non-negative • they may be equal to zero with positive probability ”
Statistical Postprocessing of Ensemble Precipitation Forecasts by Fitting Censored, Shifted Gamma Distributions* (2015, methodology): “As a benchmark we use the basic model defined by (11) and (12), which has only three parameters and is comparable in terms of model complexity with the basic ExLR model by Wilks (2009). Direct comparisons of the parametric postprocessing approaches mentioned in section 1 (ExLR, BMA, EMOS) suggest th”
Extending logistic regression to provide full‐probability‐distribution MOS forecasts (2009, methodology): “…f (x) (Equation (8)) involving the GFS ensemble mean that were tried included using√ xens or xens only, whereas other authors (Hamill et al., 2008; Sloughter et al., 2007) have reported that better results were obtained with different transformations of the ensemble-mean precipitation predictor.”
Probabilistic quantitative precipitation field forecasting using a two-stage spatial model (2008, methodology): “The method builds on the two-stage model of Sloughter et al. (2007) developed for precipitation forecasts at individual sites, and extends it by accounting for spatial correlation.”
Locally Calibrated Probabilistic Temperature Forecasting Using Geostatistical Model Averaging and Local Bayesian Model Averaging (2011, background): “(2005), has enjoyed success in forecasting weather quantities such as 2-m temperature, sea level pressure, precipitation (Sloughter et al. 2007), and wind speed (Sloughter et al.”
Combining Predictive Distributions (2011, background): “Discrete and mixed discrete-continuous predictive cumulative distribution functions also occur frequently in practice, such as in quantitative precipitation forecasting (Sloughter et al. 2007) and for count data (Czado et al.”