Statistical Downscaling of Extreme Precipitation Events Using Censored Quantile Regression

By Petra Friederichs, Andreas N. Hense. 2007

204 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0aedf7f0595dea1c4e2e2d5bce6d17c879a12436

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Citations in the corpus

Strictly Proper Scoring Rules, Prediction, and Estimation (2007, background): “232), and Friederichs and Hense (2006) for measuring insample goodness of fit and out-of-sample forecast performance in meteorological and financial applications.”
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.”
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).”
Forecast verification for extreme value distributions with an application to probabilistic peak wind prediction (2012, unknown): “1/u, otherwise (Gneiting and Raftery, 2007; Friederichs and Hense, 2007).”
Stochastic Model Output Statistics for Bias Correcting and Downscaling Precipitation Including Extremes (2014, methodology): “To quantify the capability of our model to predict specific quantiles, we employ the quantile skill score (QSS; Friederichs and Hense 2007; Friederichs and Thorarinsdottir 2012).”
Quantile regression neural networks: Implementation in R and application to precipitation downscaling (2011, methodology): “For the uncensored models, the qrnn ﬁtting function and the rq optimizer from quantreg gave similar results, with values of QVSS falling within convergence tolerances of zero in all trials.”
Robust bias-correction of precipitation extremes using a novel hybrid empirical quantile-mapping method (2021, background): “Climate data products with fine spatial resolutions, which are important for studying localized changes in extreme climate events, can be generated by combining statistical and dynamic downscaling, (Friederichs and Hense 2007).”
Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE (2012, methodology): “Precipitation is bounded by zero and might be represented by a censored process, r(Y*) 5 max(0, Y*) (Friederichs and Hense 2007).”