Probabilistic Forecasts of Precipitation in Terms of Quantiles Using NWP Model Output

By J. Bremnes. 2004

156 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/b4a6eb2f69f5be79ade2f10beaab4c3d80ccbf0f

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Citations in the corpus

Modeling tropical cyclone intensity with quantile regression (2009, methodology): “In the meteorological literature, Bremnes (2004) shows how to produce reliable probability of precipitation forecasts using quantile regression and Friederichs and Hense (2007) show how to use quantile regression to downscale forecasts of extreme precipitation from the reanalysis data.”
Estimation of High Conditional Quantiles for Heavy-Tailed Distributions (2012, methodology): “Quantile regression has been used for estimating tail quantiles in many different studies, such as precipitation downscaling (Bremnes, 2004a ; Friederichs and Hense, 2007 ), wind power forecasting (Bremnes, 2004b ) and Value-at-Risk estimation (Taylor, 2008 ).”
Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: A comparison (2014, methodology): “…for applications in the economic sciences, it has since been introduced into environmental modelling and climate change impact assessment (e.g. Bremnes, 2004; Nielsen et al., 2006).115 The technique has been applied in various research studies as a post-processing technique to estimate…”
Quantile regression neural networks: Implementation in R and application to precipitation downscaling (2011, background): “…in the environmental sciences to climate trend detection (Koenker and Schorfheide, 1994), ozone pollution modelling (Baur et al., 2004), wind power forecasting (Bremnes, 2004b), ecological modelling (Cade and Noon, 2003), and precipitation downscaling (Bremnes, 2004a; Friederichs and Hense, 2007).”
Probabilistic quantitative precipitation field forecasting using a two-stage spatial model (2008, background): “They include linear regression [Glahn and Lowry (1972), Bermowitz (1975), Antolik (2000)], quantile regression [Bremnes (2004), Friederichs and Hense (2007)], logistic regression [Applequist et al. (2002), Hamill, Whitaker and Wei (2004)], neural networks [Koizumi (1999), Ramirez, de Campos Velho…”
Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging (2007, methodology): “…techniques such as model output statistics (MOS) can be used to generate probabilities of exceeding thresholds (Glahn and Lowry 1972; Klein and Glahn 1974; Bermowitz 1975; Charba 1998; Antolik 2000), or to generate quantiles of expected precipitation (Bremnes 2004; Friederichs and Hense 2007).”
Strictly Proper Scoring Rules, Prediction, and Estimation (2007, background): “For instance, probabilistic quantitative precipitation forecasts involve distributions with a point mass at zero (Krzysztofowicz and Sigrest 1999; Bremnes 2004), and predictive distributions are often expressed in terms of samples, possibly originating from Markov chain Monte Carlo.”
Probabilistic forecasts, calibration and sharpness (2007, background): “The average width then is insufficient to characterize sharpness, and we follow Bremnes (2004) in proposing boxplots as a more instructive graphical device.”
Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE (2012, methodology): “This is in accordance with approaches by Hamill et al. (2004) and Wilks (2009), who used the ensemble mean, or Bremnes (2004), who used quantiles and minima and maxima within the ensemble, as well as relative frequencies of precipitation.”