Comparison of Methodologies for Probabilistic Quantitative Precipitation Forecasting

By Scott Applequist, Gregory E. Gahrs, R. Pfeffer, Xufeng Niu. 2002

92 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/03ab821b2443d4dbeb4380020f40ca383641cf6b

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. J. Bremnes
2. D. Wilks
3. Tilmann Gneiting
4. Adrian E. Raftery
5. Petra Friederichs
6. Mohammad Valipour
7. Achim Zeileis
8. Jakob W. Messner

In Global and Planetary Change

1. J. Bremnes
2. D. Wilks
3. Tilmann Gneiting
4. Adrian E. Raftery
5. Petra Friederichs
6. Achim Zeileis
7. Jakob W. Messner
8. Georg J. Mayr

Citations in the corpus

• Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging (2007, unknown): “Applequist et al. (2002) found that logistic regression can outperform standard regression, and Hamill et al. (2004) found that this can be further refined by using logistic regression on power-transformed forecasts.”
• Linear and Nonlinear Statistical Downscaling for Rainfall Forecasting over Southeastern Brazil (2006, methodology): “Comparing the results of ANNs used for downscaling with other techniques (Koizumi 1999; Hall et al. 1999; Applequist et al. 2002) it was shown that ANN improved rainfall forecasts, mainly in situations associated with high and moderate rainfall rates.”
• Probabilistic Forecasts of Precipitation in Terms of Quantiles Using NWP Model Output (2004, methodology): “In this paper probability of precipitation is modeled by means of probit regression, but other methods that provide estimates of probabilities could be used as well; see Applequist et al. (2002) among others.”
• Postprocessing of Ensemble Precipitation Predictions with Extended Logistic Regression Based on Hindcasts (2012, unknown): “Applequist et al. (2002) have compared five different linear and nonlinear statistical models to produce 24-h probabilistic quantitative precipitation forecasts for accumulations exceeding thresholds of up to 2.5 mm.”
• Extending logistic regression to provide full‐probability‐distribution MOS forecasts (2009, methodology): “Logistic regression appears to have been used relatively infrequently for MOS post-processing of conventional single-integration dynamical-model forecasts (Lemcke and Kruizinga, 1988; Vislocky and Young, 1989; Sokol, 2003; Detlefsen, 2006), even though performance of logistic-regression MOS compares”