Comments on “Comparing Area Probability Forecasts of (Extreme) Local Precipitation Using Parametric and Machine Learning Statistical Postprocessing Methods”

Bob Glahn Meteorological Development Laboratory, NOAA/National Weather Service/Office of Science and Technology Integration, Silver Spring, Maryland

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Corresponding author: Bob Glahn, harry.glahn@noaa.gov

The original article that was the subject of this comment/reply can be found at http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-17-0290.1.

For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Bob Glahn, harry.glahn@noaa.gov

The original article that was the subject of this comment/reply can be found at http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-17-0290.1.

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  • Applequist, S., G. E. Gahrs, and R. L. Pfeffer, 2002: Comparison of methodologies for probabilistic quantitative precipitation forecasting. Wea. Forecasting, 17, 783799, https://doi.org/10.1175/1520-0434(2002)017<0783:COMFPQ>2.0.CO;2.

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  • Glahn, B., 2014: A nonsymmetric logit model and grouped predictand category development. Mon. Wea. Rev., 142, 29913002, https://doi.org/10.1175/MWR-D-13-00300.1.

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  • Whan, K., and M. Schmeits, 2018: Comparing area probability forecasts of (extreme) local precipitation using parametric and machine learning statistical postprocessing methods. Mon. Wea. Rev., 146, 36513673, https://doi.org/10.1175/MWR-D-17-0290.1.

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  • Wilks, D. S., 2009: Extending logistic regression to provide full-probability-distribution MOS forecasts. Meteor. Appl., 16, 361368, https://doi.org/10.1002/met.134.

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  • Wilks, D. S., and T. M. Hamill, 2007: Comparison of ensemble-MOS methods using GFS reforecasts. Mon. Wea. Rev., 135, 23792390, https://doi.org/10.1175/MWR3402.1.

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