Probabilistic Quantitative Precipitation Forecasts Based on Reforecast Analogs: Theory and Application

By Thomas M. Hamill, J. Whitaker. 2006

292 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/91235347706cc41460baf1efdad90a67701083d4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. D. Wilks
2. Xuguang Wang
3. Thomas M. Hamill
4. Aaron Johnson
5. J. Whitaker
6. R. Hagedorn
7. Jonathan A. Weyn
8. C. Bishop

In Global and Planetary Change

1. D. Wilks
2. Xuguang Wang
3. Thomas M. Hamill
4. Aaron Johnson
5. J. Whitaker
6. R. Hagedorn
7. C. Bishop
8. Geir Evensen

Citations in the corpus

• Statistical Postprocessing of Ensemble Precipitation Forecasts by Fitting Censored, Shifted Gamma Distributions* (2015, unknown): “…2014) while the consideration of precipitable water as an additional predictor can yield some improvement during the warm season where the forecast precipitation amount is more sensitive to the vagaries of the convective parameterization and its triggering scheme (Hamill and Whitaker 2006).”
• Kalman Filter and Analog Schemes to Postprocess Numerical Weather Predictions (2011, background): “2006; Ren and Chou 2007); longrange weather (Bergen and Harnack 1982; Livezey and Barnston 1988; Toth 1989; Xavier and Goswami 2007), sea ice-anomaly prediction (Chapman and Walsh 1991), shortterm visibility (Esterle 1992), mesoscale transport forecasts (Carter and Keislar 2000), El Niño–Southern Os”
• Verification and Calibration of Neighborhood and Object-Based Probabilistic Precipitation Forecasts from a Multimodel Convection-Allowing Ensemble (2012, methodology): “…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 neural networks (Yuan et al. 2007, 2008), nonhomogeneous Gaussian regression (NGR; Gneiting et al. 2005;…”
• Postprocessing of Ensemble Precipitation Predictions with Extended Logistic Regression Based on Hindcasts (2012, unknown): “…Prediction (NCEP) Global Forecast System (200-km grid spacing, 15 members, every day during a 25-yr period; Hamill et al. 2004; Hamill and Whitaker 2006; Wilks and Hamill 2007), and for the ECMWF EPS (80-km grid spacing, 15 members, weekly, during a 20-yr period; Hagedorn et al. 2008;…”
• Comparison of Ensemble-MOS Methods Using GFS Reforecasts (2007, methodology): “These biases may be due to model errors, insufficient resolution (Weisman et al. 1997; Mullen and Buizza 2002; Szunyogh and Toth 2002; Buizza et al. 2003) or suboptimal parameteriza- tions, suboptimal methods for generating the initial conditions (Barkmeijer et al. 1998, 1999; Hamill et al.”
• WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting (2020, background): “They take as input the instantaneous”
• Probabilistic Forecast Calibration Using ECMWF and GFS Ensemble Reforecasts. Part II: Precipitation (2008, methodology): “2 Previously, Sloughter et al. (2007) used a one-thirdpower transformation in their precipitation calibration, and Hamill and Whitaker (2006) used a one-half-power transformation in the logistic regression of daily precipitation forecasts.”